This week, OpenAI unveiled its answer to Meta’s Muse and Grok Bots with Dots. We’ll take a closer look at what they can do and how this fits into the wider trend of ambient agents.

Plus, why one leader hates AI generated decks and how companies are evolving their UX beyond traditional chat boxes.

This is your Product Briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

New from the Department of Product Substack this week:

How Stripe built an internal prototyping tool that’s created 12,000 prototypes so far
Stripe’s new internal tool prototyping has quietly changed how a quarter of the company works. Designers skip engineering queues, finance teams turn spreadsheets into clickable dashboards, and sales builds its own ROI calculators. In this week’s Deep Dive, I break down exactly how this works, the tech stack behind it, and how to build your own version. (Department of Product)

How Uber centralised AI agent integrations with an MCP Gateway
Uber’s gateway hosts over 800 MCP servers and 5,000+ tools, with an AutoCrawler that turns existing service definitions into agent tools. Useful for any team deciding how to expose internal services to agents without hand-building every connector. (Uber)

The growing premium of Originality in product design
Scott Belsky argues that as mimicry gets easier, human flaws, taste, and real creative risk become the new premium goods. Worth a read if you’re wondering what AI can’t replicate. (Adobe Design)

Why AI is coming for middle management - and what to do about it
Cloudflare’s CEO lays out his case for why AI is coming for middle management - and what you can do about it. (The Verge)

The mental shifts leaders need as AI accelerates the pace of business
Only 25% of CHROs trust their leaders to perform well in an AI-driven world, and just 12% of leaders say they’re skilled at navigating emotions at work. HBR examines the mental shifts required to navigate the speed of AI-induced business change. (Harvard Business Review)

OpenAI’s Codex product leader on the One True SaaS Layout

New product features and innovation this week

This week, OpenAI launched its take on always-on ambient bots that can get stuff done on your behalf, with the unveiling of Dots. Dots are persistent agents powered by GPT-6 Astra that each get their own cloud computer and connect to more than 4,000 different apps through plugins that can be used from ChatGPT, Slack or Teams.

Dots works through layers, which means you can give your Dot the context it needs and it retains it across all of the apps you’ve enabled. When a Dot is idle and not doing anything it does “proactive research”, using read-only access to your connected apps, meaning it call look for opportunities to help without being able to take actions like sending messages.

For actual work, it then uses an “auto-review” system that checks planned actions against your instructions and custom rules and makes decisions about what it can do autonomously and what needs your permission.

Like Meta’s Muse, Dots have been anthropomorphised into cute, furry little creatures, which means we’re now in the strange stage of the AI hype cycle where AI agents are simultaneously predicted to destroy humanity by building bioweapons, destroying digital infrastructure, and doing all sorts of other things we can’t yet comprehend and also help us with editing Google Docs in the shape of a furry blue blob wearing a beret.

For product teams, this idea of always-on, ambient assistants that surface actions for approval in the background is a growing trend that’s worth keeping an eye on. Recent other examples include DoorDash agents that automatically restock food in offices, Atlassian’s vulnerability scanner and Cursor agents that auto-flag bugs.

Dots in action

Here’s a full demo of Dots in action from OpenAI engineer Holly Li:

Sam Altman also shared his own personal Dots set up which you can see here.

Other items from OpenAI’s demo day worth knowing about

Aside from Dots, the company also revealed a new B2B marketplace, with launch partners including Notion, Glean, ElevenLabs, Figma and more. For enterprise companies, the marketplace means that they can automatically redirect their existing budgets towards these third parties without having to go through a separate procurement process.

OpenAI also launched Space - a new collaborative doc space for people and agents, which looks a lot like Notion or Google with agents as a user.

New features you can use at work

Synthesia has launched conversational surveys that bring your surveys to life through an avatar which asks users questions conversationally, rather than through static forms, which could be helpful if you’re looking for ways to make your surveys more engaging.

Replit shipped interactive charts in Agent chat, alongside a harness that lets the model route itself. Users ask for a visualisation and get a shareable chart inline. The Agent’s core model now picks its subagent tier and effort per task, and Replit claims it beats a sidekick architecture by 11 and 16 points on DeepSWE and Terminal-Bench. Cursor shipped a similar /visualize command that renders charts and diagrams inline in the Agents Window.

Google’s Design tool, Stitch, gained a new CLI that lets you connect coding agents to generate design systems.

Linear’s coding agent now auto-routes across models from OpenAI, Anthropic and open-source providers. It can be assigned from Linear issues or from Slack with @linear, and Loops run scheduled or triggered work such as bug fixes, documentation and clean-up.

Figma’s Dev Mode is now available in Codex with a plugin and MCP support. The plugin shows what is ready for development, letting you compare local builds with production.

New in Product Intelligence - Visual Search

Visual search is a new feature which scans the latest components from features and designs from the world’s leading companies and lets you ask hyper-specific questions like “How do AI agents ask for permission” to use in your product’s design.

Perspectives from the world’s leading product leaders

Sarah Sachs, Head of AI Engineering, Notion, re-emphasised her view that token usage is a vanity metric. She says the real work is cutting latency and cost through precise tool definitions and model routing, and that success means growing ARR while dropping down the token leaderboard.

Geoff Charles, CPO, Ramp, argues that coding is now cheap, so the bottlenecks to shipping are fragmented customer signals, slow decisions, manual QA and launch coordination. In other words, most of the things that PMs traditionally have to deal with.

Edward Kim, CTO, Gusto, argues AI needs enough business context to act rather than just answer. He says Business Compliance shipped in 6 months instead of 12+ because Gusto already detected when customers hire in new states.

Stripe’s Senior PM, Jeff Weinstein sees new consumer agents launching with different product focuses, singling out Pickle for group decisions and noting Link lets agents pay on a user’s behalf. He also says Stripe already suggests WebMCP is becoming an increasingly important protocol, saying agents are gaining traction on WebMCP checkout across Meta wearables, Kernel browsers, Cloudflare workers and Shopify.

Grant Lee, CEO, Gamma, says AI-generated decks are “slop filled with the average of every pitch deck it has seen” and that “AI can’t be held accountable”, so ideas must be human-owned.

📈 Product data

FullStory’s headless MCP volume grew 25x from April to September, as agents watch session replays instead of human analysts.

GPT-6.1 Sol scored 36.1% on AutomationBench at about $0.29 per task, versus about $0.88 for Opus 5.5 at the same score. Cheaper tiers are now close enough on agentic work to justify routing. But Google’s Gemini now leads the field, though, with an impressive 51.3%.

Deel says its internal Akai agent platform added more than $140M in ARR in 90 days by automating work equivalent to roughly 600 full-time employees, without adding headcount.

Meta’s Muse now has more than 3 million users who give the AI agent at least one prompt a week, according to internal data reviewed by The Information.

OpenAI says ARR is nearing $70 billion, with B2B revenue up 100%+ since July while Anthropic’s IPO prospectus, as reviewed by the Financial Times and Reuters, shows a 2025 operating loss above $8B on revenue of about $4.6B, Q2 2026 revenue of $11.5B, a $518B infrastructure plan, and nearly 25% of 2025 revenue from two customers.

New research from Twilio shows that companies assume users want human-like AI when many users don’t care. A recent study showed 54% agreed that human-like personalities are important but nearly half don’’t care - they just want help.

MongoDB says its internal agent workspace Holly cut seller prep from 4-6 hours per account by 95%.

A new study finds short‑form video addiction steadily undermines adolescents’ ability to cope with everyday school challenges. Adding a doomscrolling video social feed to your product has real world implications.

🔮 Emerging trends to keep on top of

Based on analysis of over 5,000 signals powered by Product Intelligence, here are some emerging trends for product leaders and teams to watch.

Upgrade to unlock the full report

1/ Agents are moving from on-demand to scheduled and proactive work

Agents are starting to act on a schedule without waiting for a prompt. OpenAI’s Dots are one example of this but other products shipped new features this week that demonstrate the same trend:

Perplexity’s Computer gained event-triggered and scheduled Automations across Slack, Gmail, Outlook and Linear and Monday.com added scheduling to Sidekick for recurring daily briefs and meeting prep. Klaviyo’s Composer now surfaces missed revenue unprompted and runs scheduled reports, while Grok Bot alerted a user mid-journey that a flight change left an Uber reservation outdated.

2/ Ecommerce and payments is being rebuilt for agent buyers

This week, Shopify added support for WebMCP checkout tools that let agents edit and Submit orders with buyer authorization. Shopify also announced that Shop Pay now works inside the viral payment agent, Instinct.

Stripe is building a new API that lets a business prove an agent is acting on behalf of a verified Link user and and DoorDash introduced a sneak preview of SMS as a new way to pay for orders, signalling SMS as another potential payment platform in the future.

3/ Interfaces are evolving beyond chat and page-based editing

As more products converge around the (rather dull) idea of an empty chat box as the default UX of choice, some product teams are starting to experiment with bolder ideas.

Shopify’s new Canvas feature lets users see all store pages on one infinite canvas that comes with an AI Sidekick in place to perform actions.

Runway introduced a new visual OS called “Project Continuum” built around real-time video interfaces and Airbnb decided to ditch the idea of a building a new chat interface, instead merging it with its existing search bar that can now understand conversations and voice requests.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials plus DoP Deep dive and Intelligence reports for in-depth analysis to learn product lessons from the world’s top tech companies.

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It took one engineer just 40 days to build - and since then it’s been used by 25% of the entire company to generate over 12,000 prototypes.

Harbour is Stripe’s new internal prototyping platform that lets anyone at the company go from a brief description of what they want to build to a realistic, shareable interactive mock-up. Authors choose their own design system, the agent checks its work and the output is rendered directly in the browser.

The result is that desires go from static to interactive with little engineering input, reviews center on a live prototype and feedback is attached directly to that prototype in the browser.

Stripe’s engineering lead says it’s one of the few internal tools they’ve built that transformed how they work “seemingly overnight”:

Prototypes have also now expanded beyond product and into other areas like finance, risk and sales - with internal teams becoming less reliant on product and engineering to build internal apps and dashboards that would previously have sat in a prioritisation backlog.

Stripe’s new internal prototyping tool is a reflection of the changes that are happening across the product industry; designers can now build fully fledged prototypes with little engineering input and non-engineers are now building their own internal tools. A recent report by Lovable1, for example, found that internally built software applications grew from just 11 in April to over 80 in August.

In this Deep Dive we’ll take a closer look at what Stripe built, unpacking the technologies behind it to explore how you might be able to build something similar of your own.

As well as this, we’ll take a look across the industry to uncover other examples of how the design / prototyping process is changing.

Coming up:

• Stripe’s new prototoping tool explained - what it is, what it can do and the technologies powering it

• How to build your own version of a powerful prototyping tool using the same principles as Stripe

• What other companies are doing - the new tools and technologies reshaping how product teams approach design and prototyping

• Perspectives on what the future of design might look like

Stripe’s new Internal Prototyping Tool Explained

Harbor is Stripe’s new internal AI-assisted prototyping tool that is the latest in a growing list of internal products at the company, following other tools like Minions (AI coding) and Kai (internal knowledge platform).

Here’s a snapshot of what it is and how it works:

Harbor lets anyone at Stripe describe an interface they want to build in their own language and get back a functional, interactive prototype that’s built with Stripe’s own design components and realistic mock data without writing code.

Stripe says it was originally built by a single engineer, Cristian Rivera in approx. 40 days and the original scope of the initial version was to help designers transform their static designs into something more interactive so they could test whether ideas worked alongside PMs - without taking up engineering resources.

Since that original version, the tool has expanded beyond just product design and across the entire company with over 12,000 prototypes now built since May this year.

What it can do and the technologies underpinning it

To build a prototype, the author picks a design system to use (so, for example, one prototype might be an in-app design and another might be marketing page related), they describe what they want to build, and the agent writes a multi-file project to match.

The agent can read the rendered page, switch the viewport between tablet and desktop and automatically write up reports of the errors it found along the way so that it can come back to fix them.

Read more

This week, Meta’s Muse agent crossed over 500,000 users and made the jump into an impressive looking new piece of hardware, but Amazon has banned it. We’ll take a closer look at the details.

Plus, Figma’s CEO explains why he thinks UIs won’t disappear and the AI Agent safety concerns continue, prompting companies to ship new Agent auditing capabilities.

This is your Product Briefing. Have a great week ahead!

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Key reads and resources for product teams

Making Sense of the New Product Development Models
Spotify doubled its AI assisted code in a year, but faster output created new problems further down the pipeline. I dig into this alongside similar signals from Linear, Ramp, PostHog and Square to map five core shifts reshaping product development. (Department of Product)

How Dropbox built its new API documentation
This week, Dropbox’s product team gave its API documentation a significant upgrade, and you can now test endpoints right in your browser without ever touching a separate tool. If you’re curious about what API docs look like in 2026 this case study is an interesting read. (Dropbox)

Stripe’s CEO explains how Stripe uses Claude Code internally

In this conversation, Patrick Collison explains how Stripe used Claude Code to build products like Stripe Projects in weeks instead of months and why “every codebase is now a prompt for another codebase”. (Anthropic)

How Sentry built an internal evals platform from scratch and now runs 1,800 eval runs a month on pull requests
Sentry started in May and now runs evals across 17 AI product surfaces using production-derived scenarios rather than synthetic tests, reporting 20-50% cost reductions and 10-20% eval-pass improvements. (Sentry)

CAFE(S): Atlassian’s new framework for evaluating the context you hand to an agent
Atlassian names five properties for context quality, clarity, actionability, fidelity, efficiency and security, and treats token spend on context as a real budget line. It also cites the EchoLeak prompt-injection exploit against Microsoft 365 Copilot as the first documented real-world exfiltration of this kind. (Atlassian)

New product features and innovation this week

Meta continued the aggressive rollout of its Muse Agent with the release of a bunch of new features and integrations.

A new GitHub integration lets users review pull requests, manage issues and notifications, an Expedia integration lets Muse autonomously book hotels and travel logistics from a simple destination request and Meta’s AI chief Alexandr Wang confirmed that Muse is also getting its own email address.

Alongside these new use cases, Mark Zuckerberg also announced a major new piece of hardware with the announcement of Muse Charm:

Charm is a small tamagotchi-sized toy that can attach to your keyring to bring Muse to wherever you are. The form factor here seems pretty smart; previously, companies have attempted to ship devices designed to either replace your phone or force users to carry an additional clunky device alongside it.

Most of these previous attempts have failed miserably (see Rabbit and Humane Pin), but in a world where most users still carry around their keys, shipping a small device like this that attaches to those keys feels like something users would be willing to do - and it could be the start of a new form factor we see from other companies like OpenAI who are working on a small device of their own.

But, it’s not all good news for Meta. Amazon confirmed that the agent is now officially banned from making purchases on Amazon. Amazon says it was never told by Meta that it would access its stores and that Muse doesn’t identify itself correctly when it browses. Amazon lost a previous lawsuit against Perplexity’s Comet browser in August and so it’s unclear whether it will be successful in blocking Muse in the long term.

Google launched Gemini 3.8 Live with Avatars, designed to bring a more conversational experience to users. Google says that by pairing near real-time video generation with speech, it enables product teams to expand their virtual offerings for use cases like customer service and interactive walkthroughs. Here’s some examples of it in action:

New features you can use at work

Atlassian shipped a new tool called Protobox for AI-assisted design and prototyping. This is a new Mac app, built over 18 months that embeds coded prototypes and agent skills to help product teams shape new ideas through prototypes. Atlassian says it’s already used internally across teams at Confluence and Loom. Atlassian’s VP of Core Design explained how they built it here.

Linear built a dedicated bot that turns social media mentions into structured product feedback that can be used to shape new features or iterations of your product:

UserTesting released new MCP servers for research recruitment across the UserTesting and User Interviews platforms. Product teams can recruit participants, launch studies and validate AI-generated concepts from inside ChatGPT, Claude, Figma Make or Gemini.

Asana launched Command, a new planning system built for software delivery. It gives engineers and coding agents shared context, updates release forecasts dynamically as work changes and surfaces delivery risk without status meetings

Perspectives from the world’s leading product leaders and operators

Dylan Field, CEO, Figma, says that user interfaces will not disappear as agent adoption continues to grow, because humans need human-readable ways to audit what an agent did. He expects new input modalities like voice and VR to expand the design surface rather than shrink it, with design becoming “the new code”.

Mike Cannon-Brookes, Co-founder and CEO, Atlassian, argues that software factories are mostly hype, comparing real progress to adding machinery to a chocolate factory station by station rather than a revolutionary leap. Humans stay essential for initiation, judgment and exception handling, and he warns against the “I do AI better than you” positioning that’s becoming more common.

Cristina Cordova, COO, Linear, predicts that consumers won’t actually delegate spending or personal communication to agents yet, drawing a hard line between low-stakes tasks like restaurant bookings and access to credit cards and inboxes. She puts current agent payment capability at the novelty stage for anything higher-stakes.

Tom Eggemeier, CEO, Zendesk, argues AI spend should be measured in cost per completed task rather than token price, describing an internal practice of routing different work types to different specialised models that cut average cost per task 22% in a month.

Thariq Shihipar, Member of Technical Staff, Anthropic, says that product development teams should resist shipping 10x more features simply because models got more capable. Instead, he argues that teams should spend the time on user research and prototyping.

📈 Product data

OpenAI has published a new mental health benchmark called MentalHealthBench and says that Astra GPT-6 scores the highest. OpenAI worked with more than 80 licensed psychologists and psychiatrists from 22 countries to build the benchmark and you can read the full methodology here but for product teams building AI Assistants that need a strong ability to navigate complex mental health issues, the paper is worth a read.

Meta says Muse passed 500,000 total users, 250,000 daily active users and 2 million prompts submitted in its first week.

Proton surveyed 4,014 chatbot users across France, Germany, the UK and the US and found 66% discuss sensitive topics such as finances and mental health with AI, while only 11 to 20% trust AI companies to protect that information and 80% want a chatbot built specifically for privacy.

Claude Opus 5.5 is the best model for operational tasks, scoring 40% on Zapier’s AutomationBench which tracks 657 workflows. OpenAI’s new model, GPT-6 Sol, scored 33.2% on Zapier’s AutomationBench, up from 28.77% for GPT-5.6 Sol. For context, in April the highest scoring model was scoring 10%.

Lovable’s annualised revenue reached $600 million, up from $500 million in June, with users at two-thirds of Fortune 500 companies and nearly a billion monthly views on apps built on the platform.

LinkedIn reports that verified members get 50% more post engagement and nearly 90% more profile views, and is now piloting peer-to-peer confirmation of shared work or school history.

Salesforce found agentic search, where a shopper’s first step is a question to an AI assistant, grew 200% year on year, with AI-referred traffic up 150-428% every quarter since 2024.

Vercel’s Agent Skills registry hit one million skills and 280 million installs in just seven months, faster than GitHub reached one million repos (27 months), the App Store reached one million apps (63 months), or npm reached one million packages (117 months).

Dropbox says that 70% of code at the company is now AI generated.

Emerging new trends powered by Product Intelligence

Here are some of the new emerging product trends uncovered this week, powered by Department of Product Intelligence. Paid subscribers can get access to the full datasets underpinning these trends with over 150+ data points here:

Upgrade to unlock Intelligence Reports

1/ Users are being given more control over their algorithm and UX

YouTube shipped a major new feature this week which gives users the ability to craft their own custom feeds, reflecting a growing trend of users being given more control over the algorithms that power the products they use:

Spotify has also officially expanded its Taste Profile to users in the US which lets users directly shape their recommendation and homepage feeds and Meta’s AI Chief said that Muse’s new Feed could be a sign of things to come where personal agents shape feeds based on their rich memory of conversations, comparing it to a “good friend” recommending content rather than traditional algorithmic systems.

2/ Token spend efficiency is top of the agenda - with companies flexing their ability to reduce costs

Vendors are increasingly competing on token efficiency as companies look to reduce their AI token spend.

AWS has released an open-source agent harness which it claims reduces token costs by 28% and Stripe has become the latest company to adopt the WebMCP standard - which it says reduces agent token cost by 42% during payment flows. You can read more about WebMCP and why it matters to product teams here.

Elsewhere, Cursor says it can now offer users a 7% reduction in token costs thanks to tighter prompts, better caching and compressed reads and Linear added adaptive routing to its Coding Agent product this week, where it automatically delegates simpler tasks to faster models, keeping more expensive reasoning models for more complex work alongside open source.

Leaders are also starting to share their own perspectives in managing token spend.

Harvey’s co-founder said the company went from negative 50% to positive in a single quarter through model routing, harness improvements and training and Databricks’ VP of engineering said that giving 3,500 engineers access to GPT-6 Astra actually increased token spend by 60% - but only on complex work. They now run cohort-based experiments on smaller groups of engineers who are given a budget to experiment with model choices:

3/ Keeping AI agents under control is emerging as a distinct problem - and product category of its own

AI agents are continuing to run wild with more attacks revealed this week.

OpenAI agents were found to have breached the US Commerce Department, the SEC, and attempted access to the Education Department, plus Australian government health databases and university systems dating back to at least March 2026, with one successful compromise of Australia’s national healthcare system in June that OpenAI did not learn of until August.

Meta’s Muse Mac app had two separate security incidents, including one in which allowed a local code execution to hijack the agent via an undocumented setting.

As a result of these ongoing concerns around AI agents, platforms are scrambling to release new features designed to help product teams observe the behavior of AI agents.

This week saw the release of Agent Observability in Snowflake, a new Workflow history feature in Attio and a new tool from AWS to help teams observe AI agents in applications.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials plus DoP Deep dive and Intelligence reports for in-depth analysis to learn product lessons from the world’s top tech companies.

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This month marks just over 3 years since the Department of Product launched on Substack and I wanted to reach out to say thanks a lot for your support during that time!

Over the past 3 years, we’ve grown to over 70,000 subscribers across our platforms and we’ve always tried our best to unpack everything that’s happening each week and transform that into actionable insights you can use to shape your own product’s strategy.

At the start of 2026, I spent some time thinking about what the next chapter of the Department of Product might look like and how we could continue to deliver on that same principle of translating market signals into something that’s actionable.

And today I’m delighted to announce the launch of Department of Product Intelligence.

The Department of Product Intelligence is a powerful new platform built specifically for product leaders and operators who want to stay at the very forefront of what’s happening in tech and how the world’s top product teams operate - and translate that into product strategy.

It’s a tool that continuously scans and judges tens of thousands of market signals from the world’s leading product teams to help you understand what’s happening right now and why it might matter to your product.

Request a Demo

Here’s a snapshot of some of the things it can do.

Get instant answers to product questions that matter

The Product Intelligence platform scans tens of thousands of market signals from the world’s leading companies and judges them through one single lens: does this matter to product teams?

If it does, it makes it into the corpus, if it doesn’t, it’s dropped.

With real time product market signals at your fingertips you can get instant, up to date answers to questions like:

1. What UX patterns are emerging in new AI features?

2. What new internal tools are companies building to manage their development processes?

3. How are companies pricing their AI features?

4. What new trends are relevant to my company?

5. What do product leaders say about the future of UX?

6. How is in-product search changing?

And while getting answers to specific questions is helpful, where Product Intelligence becomes really powerful is when you use the signals to shape your product’s own strategy.

Reshape your Strategy with the Strategy Thought Partner

Product Intelligence scans your product’s website, builds a personalized profile of your company and lets you use this to help you shape your own strategy together with the strategy thought partner.

The Strategy Thought Partner comes with in-built access to leading product frameworks like Porter’s Five Forces, Blue Ocean Strategy and more - to let you think through your ideas strategically. The thought partner grounds its research in the corpus of 10,000+ signals and runs the relevant frameworks on your behalf, flagging any potential risks and pitfalls.

If you want to take it further, you can ask it to pressure test decisions using frameworks like pre-mortems and second order effects.

In the end, you get a structured recommendation, backed up with practical frameworks and relevant, real world market signals you can use to shape your product’s strategy.

Build DoP Deep Research reports in minutes

When I speak to product teams who rely on tools like Perplexity, Gemini and ChatGPT for product-related Deep Research, the common problem they all raise is that the output just isn’t fine-tuned enough on product-specific content. A typical Deep Research report is helpful but it often misses out the deeply specific and relevant new features or internal tools that were built that week.

DoP Deep Research is powered by the tens of thousands signals that have already been judged for product relevance meaning you only ever get served up to date, useful sources from the world’s leading tech companies.

Read sample Deep Research reports:

• In-Product Search: How the UX Is Changing

• How Companies Are Pricing AI Features

• Leaders’ Vision for the Future of Product Development

• Recent Voice Feature and UX Developments

• How Companies Are Using MCP and Going Headless for AI Agents

And if you want to turn these research reports into ongoing email Briefings for you and your team, you can.

Get Intelligence Briefings on the topics that matter to you and your business

Many of you enjoy reading through the Department of Product’s weekly briefing but I’ve always wondered: what if you could create your own Briefing that’s entirely customized to you and your business?

The Department of Product Intelligence now makes this possible.

Let’s say you work for a fintech SaaS company and you want a monthly report on new emerging agentic payment methods or how checkout UX is evolving. You can write one prompt, run the research report and then get the report sent to you on a cadence that suits you.

Some people might want an update on what product leaders think about AI safety, others might be more interested in new internal tools for data analysis.

Whatever topic you’re interested in, you’ll get a personalised briefing that focuses on the market signals you’ve asked for - with commentary about why that matters to your product if you want it.

Read some sample Intelligence Briefings:

• UX trends

• AI pricing watch

• AI features shipped

• Product leaders’ views

• Onboarding and activation

Unlike the Substack, which is written by me, these Briefings are 100% AI generated and are designed to keep you up to date with everything that matters to you. This distinction between AI and human matters because I want the Substack to remain 100% human authored - and it will.

Scan hundreds of UX tear downs of homepages, pricing pages and features - and benchmark your own designs

Product Intelligence also spans UX and design. The platform includes over 350 (and growing) homepage, pricing page and feature tear downs. Each design is scored against a structured rubric with an underlying data model which means you can ask highly specific, structured questions about design trends and patterns.

For example, you could ask:

How are companies displaying social proof on pricing pages?

And you’ll get back a structured result set with full UI examples you can use to inform your own product’s design.

You can take things a step further and upload your own product’s designs and get it scored and benchmarked against the world’s leading companies.

Upload a design or scan a specific page and in return you’ll get a structured report along with an overall score and some suggestions on how to improve your designs.

How to get access

After months of testing, the platform is now officially open and if you’re interested in getting early access to the platform, you can request a demo now - with access rolling out over the next few weeks.

During the demo we’ll explain how it works and answer any questions you might have.

Request a Demo

How does this impact the Substack?

The Substack will remain 100% human-authored but it will be powered by the insights derived from Product Intelligence and the wider market. You’ll continue to get your dedicated Weekly Briefings, Deep Dives and Knowledge Series, but you can expect to see a richer collection of data sets underpinning Deep Dives and Briefings, powered by the Intelligence platform built for product people.

Paid subscribers will get exclusive access to these new data sets that you can use in your own research and they’ll include everything from in-depth new feature reports, hiring signals, UX trends and more.

Thanks again for all your support and enjoy the rest of your week!

Rich

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Spotify has published a detailed piece outlining some of the key lessons they’ve learned along their AI development journey so far.

The tl;dr from Spotify is that after analysing their own performance:

• They’re now shipping more code than ever with merged code changes more than doubling over a year in August.

• Spotify found no direct evidence that AI assisted code caused new major production incidents and concluded that faster shipping to production with AI didn’t mean less overall investment in quality.

• In fact, they found that code quality and optimization work actually grew from 27% to 31% as a share of overall engineering work with maintenance and configuration dropping from 31% to 25%.

But, AI assistance introduced other problems downstream. As AI increased shipping velocity, other parts of Spotify’s product development process and engineering infrastructure struggled to keep up with that change with automation introducing new failure risks further downstream.

This pattern of unintended downstream effects shows up elsewhere, too.

Linear’s own analysis found that coordination overhead rose alongside a 111% increase in PR output and HubSpot spent six months building automated code review specifically because AI generated code volume overwhelmed manual review cycles.

Reading about these case studies in isolation is one thing but it’s difficult to try to look across the industry to identify shifts which impact the product development process. In this Deep Dive we’re going to take a broader look across the industry to understand some of the emerging signals across some of the world’s leading tech companies to attempt to make sense of what the new product development process looks like.

Coming up:

1. The five emerging product development themes explored in detail

2. What these new themes might mean for product teams and leaders

3. The full data set with downloadable examples and case studies you can use to shape your own development processes

Unpacking Five Emerging Themes in Product Development

This analysis was built using over 300 different data points and signals from some of the world’s top companies from the past 30-60 days only to ensure it’s up to date and relevant given just how quickly things are changing. (The full data set that underpins this research is available to download too).

Here’s a preview of some of the themes we’ll explore:

Each signal has been analysed to identify trends and themes that matter to people who build products and it is structured around five core themes that impact product teams right now.

For each theme, we’ll explore real world examples and consider what the practical impact is on the product development process.

1. The product discovery process is being transformed with always-on Agent loops

Figuring out what problems to solve for users has always been tough, and the risk with AI generated code is that product teams end up building franken-slop products packed with features that nobody really needs simply because it was technically possible to ship them.

In this sense, you could argue that figuring out what to build has never been more important.

And on the discovery side, there’s still no replacement for speaking to human users and understanding their needs. This was recently echoed by Lovable’s CEO and it’s good to see AI leaders reiterate the importance of speaking to actual human beings:

Despite that, AI can - and is - starting to play a crucial role in helping product teams solve real problems using the power of always-on agent loops that continuously discover new problems to be solved for users.

Read more

This week, Shopify’s CEO argued that one of the biggest challenges for tech companies is dealing with what he calls “AI Slop Grenades”.

Plus, new UX patterns driven by new technologies are starting to emerge. We’ll take a look at some of them including real time voice collaboration with Gemini, personal agents that can renegotiate contracts on your behalf and Snap’s new vision for “anticipatory UX” in AR glasses.

This is your Product Briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

New from the Department of Product Substack this week:

How to Develop a Headless Product Strategy
Lovable’s co-founder has a warning for product teams: stop building only for humans. He argues that companies that don’t make their products agent-friendly risk missing what he calls a considerably larger opportunity. In this Knowledge Series, I break down how to build a headless product strategy, with recent real world examples from Stripe, Rippling, Salesforce and Replit. (Department of Product)

How to use ChatGPT Work to build a semantic data layer
OpenAI’s data team explains how a semantic layer brings together key entities, metric definitions, filters, and known pitfalls (OpenAI)

233 real use cases for Meta’s Muse Agent
Meta’s personal Muse agent is having a moment. Here is a consolidated, filterable gallery of real world use cases including finance, lifestyle and productivity. (Muse Cases)

Tactics for making your company more powerful in 2026
In his latest essay, VC Paul Graham lays out the patterns behind outsized outcomes - owning the customer relationship, letting money (or tokens) flow through you, inducing network effects where none seem possible, and selling to startups before they’re big enough to matter to anyone else. (Paul Graham)

Putting the user back into AI evaluation
In this piece for Microsoft’s UX Research series, Christopher Monier, Chuck Kwong, and Wendy Wang - the researchers behind Copilot’s response-quality evaluation program - argue that machines can check accuracy, but only users can judge whether an answer actually mattered to them. (Microsoft)

New product features and innovation this week

Google has launched Gemini 3.8 Live and Extended Thinking - 2 new models that are designed to bring near real-time reasoning to conversational voice agents.

For product teams, this is clearly potentially very helpful for vanilla scenarios like troubleshooting, but over time it could introduce a new type of collaborative UX where human users work alongside agents in all sorts of creative ways that we can’t quite imagine just yet.

In this example, a user is learning programming in real time alongside agents powered by the model:

Anthropic has added Projects to Claude Code. After the change, a primary agent now manages memory and spawns subagents that keep working after you close your laptop, with proactive scheduling options.

Anthropic also shipped an update that merges Cowork and chat into one interface that auto-routes between quick answers and agentic work. As part of this Claude Design is now built in with Slides, Docs and Design. Head of Product Cat Wu said that users said they didn’t want to have to choose which product to use for individual tasks.

Snapchat launched what it calls SPECS Intelligence - an anticipatory assistant spanning iPhone, Mac, and AR glasses that surfaces relevant information without prompting. Snap is explicitly positioning against Siri and Google Assistant, with a privacy claim that personal content is not used to train models or serve ads:

Snap is describing this as a form of “anticipatory AI” that anticipates a user’s needs ahead of time and surfaces what’s relevant there and then. In a professional context, Snap says it can surface pre-meeting prep and help give you the context you need before meetings. This kind of UX has historically fallen pretty flat but it’ll be interesting to see how well it performs when it’s equipped with new frontier models.

And speaking of AI Assistants, this week Apple officially launched its new Siri - and for the first time in a long time (perhaps ever), Siri actually feels quite helpful, on a Mac at least. Apple has done an excellent job at integrating Siri into the Mac by making it the default for Spotlight with a degree of polish that should really have been there when Apple originally launched its first iteration of Apple Intelligence 2 years ago.

The relaunch adds personal context understanding, onscreen awareness, camera-based Visual Intelligence and a dedicated Siri app that syncs conversations across devices, including bill-splitting through Apple Cash and custom Wallet passes generated from whatever is in front of the camera.

Other updates worth knowing about

• Atlassian integrated the BearQ testing agent into Jira, connecting test execution to work planning.

• Pinterest is testing Restyle, letting users visualise furniture and decor in photos of their own rooms.

• Linear has extended Loops beyond issues. Agents can now edit Linear documents directly, such as auto-updating a launch plan when project dates change, post tailored updates to Slack, and trigger on changes across initiatives, projects and cycles.

• Notion added the ability to build agents that can delegate to sub-agents - each with isolated instructions, context and model.

Perspectives from world leading product leaders and operators

Microsoft’s CEO Satya Nadella argued superintelligence is only worth pursuing if it stays under human control and its benefits are diffused broadly, and said Microsoft will publish a Code of Conduct for its first-party MAI models for public consultation.

Emilie Choi, President and COO, Coinbase, says the company is pushing toward flatter, individual contributor-heavy teams to ship faster with AI, saying that the main challenge right now is to get the balance right between speed and craft quality.

Google’s Sundar Pichai used the week to restate four strategic pillars for the company’s AI work, health, disaster and weather resilience, learning, and economic opportunity, pointing to AlphaGenome’s genetic mapping work, WeatherNext 3 forecasting, and translation coverage now reaching nearly 300 languages as evidence the company is framing its AI investment as a public good alongside its commercial products

Noah Levin, VP of Design, Figma, has raised the risk of “cognitive surrender”, where the convenience of AI erodes the habits and judgment that made people good at the work in the first place. He frames it as a duality rather than a verdict, comparing it to the idea that “cars save lives, but also takes them”.

Shopify’s CEO says “AI slop grenades” are ruining workplace culture and productivity. A slop grenade is when you let AI produce the work and pass it on without adding any value (including checking it). He says that getting LLMs to produce wasteful emails that then require another LLM to condense and synthesis it is wasting people’s time at work.

📈 Product data

When AI Overviews launched in France in July, researchers had a rare opportunity to produce a clear before and after snapshot. They found that websites heavily exposed to AI Overviews lost 23.1% of their click-through rate in just nine days:

Lovable’s CEO says more of its own employees are vibe coding their own tools than ever. Its internal app ecosystem grew to 80+ apps across 17 departments by August, up from 11 in April. Clearly, Lovable has vested interest in promoting the idea of internally built vibe coded products but non-technical people building their own internal apps is still a clear theme.

OpenAI says 5 million sites have been created with ChatGPT Sites three months after launch.

LinkedIn says views of content classified as AI slop fell 40% after it shared reports with creators, and over 1 million members used its “Seems like AI slop” tool within two weeks of launch.

Airbnb says its new Insight Miner cut data investigation timelines from months to days and expanded it to dozens of teams across the company, to mostly non-technical users.

Stripe’s network data shows transaction fraud rates at AI startups were 4.3x higher than at startups overall in Q3 2025, falling to 2.6x by Q1 2026. Multi-account abuse across AI subscription companies rose 40% in six months:

Salesforce says Hunter, its new outbound sales agent, generated $500 million in pipeline last quarter.

Open weight models had a record day on Vercel’s AI Gateway report this week with 78.4% of token volume on Friday. Google’s share of Vercel gateway spend collapsed from 30% to 5%, with Gemini 3 Flash losing 95% of its token share since May.

GitHub’s COO shared research showing agent-generated pull requests have grown 9x in size over eight months, and says teams now need automated review, validation and rollback because manual review can no longer keep pace.

🔮Emerging new trends to keep on top of

1/ Vertical AI products powered by frontier labs are on the rise - and could threaten more SaaS verticals

Frontier labs are releasing vertical products designed to compete in specific industries. This week, OpenAI revealed Astra for Law which pairs a frontier model with a 230-million-URL legal index. Anthropic launched Claude for Financial Advisors with connectors to BlackRock, Addepar and Schwab, and Zendesk shipped Specialized Agents built for specific workflows. The pattern that’s emerging is a combination of frontier capability, plus a proprietary retrieval layer, plus vertical instructions, packaged up and sold as its own product.

For product teams operating in these domains, it’s unclear to what extent they might succeed.

2/ Personal and household agents are becoming a new category

Google Labs has launched CC, a family AI agent. It manages shared calendars, tasks and logistics for up to six household members, integrates with Gmail, Calendar, Tasks and Maps, and gives each member their own sharing controls.

Meta’s personal agent app Muse has officially reached #1 on the App Store one week after launch. A personal-agent app reaching consumer scale this fast is new territory. Their Chief AI Officer says Muse has already saved 100 people almost $10,000 by doing errands like cancelling contracts and renegotiating insurance policies:

For product teams, consumer agents that consolidate third party integrations into one surface makes headless integrations a priority - and threatens the traditional product UX paradigm. And aside from the UX implications, if agents are increasingly able to navigate the complexities of cancelling or renegotiating products, it could also impact retention and churn.

3/ MCP usage and headless use cases are soaring - and architecture is evolving to keep up

Anthropic’s Thariq Shihipar argues MCPs now outperform CLIs for most integration work because tool-calling has improved and MCP’s stateless design removes older constraints.

Amplitude reported 6.3 million tool calls in August alone, 5x the March volume, with product-data queries now the fastest-growing agent use case, and built a token-optimized MCP server in response. The company’s CEO even unveiled a dedicated headless product aptly named “Amplitude Headless”.

Ahrefs doubled its MCP quota across all pricing tiers after confirming that MCP is its fastest-growing product this year.

But As MCP volume increases, companies are struggling to handle it with new architectural constraints emerging. WorkOS published research showing tool definitions cost roughly 55,000 tokens per turn in a typical five-server setup, and that agent tool-selection accuracy degrades past thirty to fifty available tools, meaning every added tool taxes the selection quality of every existing one.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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Lovable’s co-founder recently predicted that SaaS won’t disappear but users will interact with fewer interfaces and websites directly.

In the future, he argues, rather than opening up 20 different tabs, users will open fewer tabs, letting agents do the heavy lifting for them. Companies that don’t leverage the opportunities of making their products available to agents, he argues, are missing out on the “considerably larger” opportunity of building for both humans and agents:

Lovable’s co-founder isn’t alone in this view, either.

One of Anthropic’s lead engineers recently said making your product headless and charging for access is akin to publishing a “make a lot of money” button, Linear’s CEO announced that agent created MCP issues now outnumber human authored ones and SaaS companies like PostHog have reported a massive 50x increase in the number of headless MCP requests in just the past 6 months:

In this Knowledge Series, we’re going to unpack some of the core elements of headless product strategy design using real world examples from companies like Stripe, Google, Microsoft, Supabase and more to help.

From figuring out what tools to expose through to payments and security, if you’re curious about how to make your product agent-friendly and potentially generate more revenue by crafting a headless product strategy, this Knowledge Series should help.

Coming up:

1. Headless product strategy explained

2. How to develop a headless product strategy of your own - 5 key steps

3. Real world examples of headless product strategy in action from Rippling, Stripe, ElevenLabs and more

4. What the future of headless might look like and the potential impact of consumer agents like Grok Bot and Muse

5. 30+ real MCP product launches from the past month you can use as inspiration

Headless product strategy explained

Designing a headless product strategy is the process of treating your capabilities, data and logic as the product - and the UI as one of many different consumption paths. These paths can be used by either humans or agents / machines:

Decoupling a product’s core logic / value UI from the UI isn’t new, of course, since APIs have traditionally served this purpose very well up until now.

What is different with this latest version of headless product design is the end user and what they’re able to do.

APIs are mostly built for developers who would read your product’s API docs in advance and write well-defined, scoped integrations built for specific actions. With the new crop of headless standards like MCP, WebMCP, A2A and others, it is now less about endpoints and more about the functionality that an agent has access to, to use in ways that they wish.

For product teams, figuring out how to navigate through these new standards to design a coherent strategy can be pretty difficult but since plenty of world leading teams have recently shipped new headless capabilities in the past few months, there’s a growing set of case studies for us to use as inspiration.

How to develop a headless product strategy of your own; key steps and considerations

Based on the current technical capabilities and recent releases from the past few weeks, here’s a collection of some of the key steps and considerations to work through when building a headless product strategy of your own.

1. Separate your product from your interface and identify high value use cases - separate your real value (logic, workflows, data) from the UI.

2. Expose it in a shape an agent can actually use - don’t just replicate APIs; define clean entities and a small, discoverable MCP/CLI/API surface that agents can reason about.

Read more

This weekend, Anthropic’s CEO has issued an urgent warning about the potentially catastrophic consequences of a “fanatically devoted collective” of AI agents. Read more about that and what other leaders are saying in response to it.

Plus, after Apple unveiled the new iPhone Duo, what could this mean for the future of mobile design? And has Meta finally built an AI product that users actually want? Their new app hit number 2 in the charts this week so we’ll look at some of the ways people are using it.

This is your Product Briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

How Uber transformed its engineering process to cut AI token spend by 52%
Uber once told engineers to max out their AI token spend, then admitted they’d blown the budget by March. Now its CTO has revealed a cost-saving system spanning developer tooling, 1,000+ MCP servers and smarter model routing that cut costs per session by 52% while agentic requests grew almost 10x. Find out how. (Department of Product)

ElevenLabs’ CEO on product deployment processes, strategy and focus as a competitive advantage
In this conversation with David Senra, Mati explains why the company stayed laser-focused on audio, borrowed Palantir’s forward-deployed engineer playbook, and more. (YouTube)

Microsoft’s research teams on rebuilding user research from the ground up
Microsoft Design talks to Ben Hanrahan, Chris Gunderson, Sara Chizari, and Bruce Phillips about how UX / user research design is being transformed with AI tooling. (Microsoft Design)

How to design apps for the new iPhone Duo
This week, Apple revealed the most significant redesign of the iPhone in years. Despite the fact the Vision Pro flopped a few years back, a major new hardware launch from Apple is still something for product teams to pay close attention to and to coincide with its release, Apple has published a guide on how to design for the new dual interfaces:

Some of the key principles Apple’s designers say to follow include:

1. Design for size classes, not screens - don’t build your layout around exact pixel measurements since the iPhone Duo changes shape constantly. Instead, apps need to recognise two situations: narrow (phone is closed) and wide (phone is open).

2. Controls belong on the side and content gets the rest - The Duo introduces a new vertical sidebar but you don’t have to redesign anything for this. Your buttons keep their relative positions, just in the new orientation:

3. Don’t ignore the fold - on the inner display, rearrange your design rather than stretch it. Use split views, two-column reflow, or a tab bar presented as a sidebar for dense apps.

Anthropic CEO Dario Admodei on why “We Must Pace the Frontier”
Dario Amodei lays out why he thinks the industry needs to deliberately slow down, and proposes a concrete three-step plan, from embedded third-party evaluators to global coordination with China, to make that happen. (Anthropic)

New product features and innovation this week

OpenAI has added GPT-6 Astra inside ChatGPT Work. The update expands Astra’s capabilities across knowledge work, including a feature that tailors output to match your own personal writing style which ChatGPT learns from emails and documents plus a new Data Analytics Agent plugin which OpenAI is explicitly marketing to product managers (among others).

You can read more about the plugin and what it does here but this is a new plugin that connects to your company data, investigates any significant changes and builds interactive dashboards on the fly that you can share with your coworkers.

OpenAI says that to interpret the data correctly, it pulls in your company’s existing business definitions and metric logic from tools like dbt, Databricks Genie Ontology, and BI dashboards, so results match what the organization already trusts.

Is Muse the start of a Meta comeback?

Meta hasn’t been on its A game recently, but this week, signs of a turnaround started to emerge after the wildly successful launch of a new personal agent called Muse.

Muse is a new freemium AI agent that can take actions on behalf of users and since its release, it’s shot to number 2 in the US App store charts.

Meta’s Chief AI officer Alexandr Wang says early Muse adoption is running well ahead of Meta’s own internal projections and users are consuming 10 times the token volume Meta’s testing cohorts had forecast during its development.

As part of the launch, he’s shared a bunch of different use cases that users have been experimenting with - and here’s a selection that are useful to know about:

• Shopping with live discounts and Whole Foods cart integration

• Purchases via Stripe Link with approval before spend

• Login access via 1Password

• Real ID application assistance

• Audio podcast generation on any topic

• Shopify Catalog products automatically surfacing for sale with zero merchant setup

New features you can use in product development work

Atlassian has announced a set of features designed to let AI agents handle software development work continuously and safely, rather than through one-off prompts. The system uses three core pillars: context, governance, and measurement.

Code Context, built on Atlassian’s Teamwork Graph, gives agents visibility into a company’s actual codebase, architecture, and past decisions. Agent Context Controls let platform teams restrict which agents can access which spaces and data. And on top of that, “agent loops” in Jira let agents automatically pick up unassigned backlog items, write and test code, and open pull requests for human review, without someone manually assigning each task

Granola added workspace-wide custom vocabulary so admins can teach the transcription system company jargon, project names, and acronyms across all team meetings. They also launched a Slackbot that answers natural-language questions about meeting decisions and follow-ups directly in Slack.

PostHog built a new service that converts user session replay DOM streams into MP4 videos so multimodal models like Gemini can analyze recordings that were previously unwatchable. For product teams, this means you can get deeper insights into user sessions and highlight potential UX problems.

Lovable added CSV/TSV files rendering as readable tables in chat (numbered rows, lettered columns, with large files previewing the first 500 rows and 50 columns), useful for designers or PMs who want to use structured data in prototypes without leaving the conversation.

Perspectives from some of the world’s product leaders and operators

Akshay Kothari, COO, Notion, argues that teams should own context and rent agent intelligence, since agents “will come and go” but compounding context is the durable asset.

George Arison, CEO, Grindr described a four-person team, none of them formally engineers, shipping production code into the live Grindr app using AI, and predicted that the ability to write software will eventually decouple from the “engineer” job title entirely. The huge caveat here is that the PMs and designers he describes have technical knowledge and / or engineering backgrounds but it’s an interesting insight into the potential future of product team composition.

Jennifer Lee, Product at Stripe, suggests that product teams who are seeing an increase in agent-driven API requests should consider monetizing these calls to create a new revenue stream. Stripe’s machine payments protocol, she argues, is ideal for this use case.

Wayne Liang, Co-Founder, HeyGen, says the interview process is changing and describes a dedicated interview round where all candidates, technical or not, complete a live task using AI agents, treating AI fluency as an assessable skill.

Gamma’s CEO Grant Lee responded to Bill Gates’s claim that AI fundamentally differs from prior technologies by arguing that AI’s transformative potential lies not in replacing human work but in making new classes of work economically viable.

📈 Product data to stay ahead

OpenAI’s researchers say Agentic workdays now outnumber human worksdays 3 to 1 in their research team. The full methodology is worth a read to avoid getting too caught up in the hype here but it’s a fascinating study of how agent work is accelerating inside OpenAI.

ChatGPT broke its MAU record for the 4th consecutive month in August, reaching 1.06 billion MAUs.

Adobe raised full-year guidance to $26 billion revenue citing AI and cloud investment, with 1 billion monthly active users across its platform.

Salesforce is in talks to acquire the user research tool Listen Labs for ~ $2 billion, a startup with about $30 million in annualized revenue. Salesforce is on an AI rampage at the moment.

MCP usage is continuing to explode across data analysis products. PostHog says its tool calls surged 50x in five months, crossing 1 million daily with 26.3 million tool calls in August alone. Amplitude separately reported 6.3 million MCP calls in August, up 5x since March.

94% of engineering leaders use AI, but only 6% have systems to scale agents across the full software lifecycle, according to a new report from Atlassian.

New data from Intercom shows that trust in AI agents is growing slowly - and that showing an agent resolving a query can help. They say that when a user watched an agent resolve a real query, user sentiment was boosted from 49% to 74%. Full Intercom AI sentiment report.

Ramp built a new internal AI on-call assistant that investigates incidents, posts findings to Slack, answers follow-ups, and prepares code fixes without manual handoff. It says In the past four weeks alone, it assisted with 575 of the 1,220 PRs merged to address incidents (about 47% of incident-fix PRs).

Uber says it halved Uber Eats search latency through a full-stack pipeline redesign, cutting retrieval latency by 50ms and reducing data lookups by more than 100x.

🔮 Emerging trends to keep on top of

1/ Real time voice APIs adoption is starting to emerge

OpenAI shipped the API version of its GPT-Live-1 model this week, combining speech recognition, reasoning and synthesis into one model. Early results show 80% fewer interruptions than turn-based systems, a 30-point improvement on Full Duplex Bench, and one healthcare customer cutting 23,000 lines of code from their voice-agent codebase.

Companies are now starting to embed these real time voice APIs into their own products.

Twilio built a native GPT-Live-1 integration into Agent Connect, handling interruption management and turn-taking without custom WebSocket code and separately shipped an ElevenLabs-Twilio call-handoff blueprint for AI-to-human escalation.

And Yelp integrated GPT-Live-1 into both Yelp Host (restaurants) and Hatch (service businesses), positioning itself among the first companies to use the new model.

2/ Structured data models are emerging as a strategic advantage

Products built around typed, semantic structures rather than free-form text turn out to work better for agents, even without explicit agent optimisation.

Linear’s CEO Karri Saarinen endorsed an outside observation that coding agents preferred writing docs in Linear over Notion, because Linear separates documents from typed entities like projects, issues and views. He argues it “wasn’t designed for agents but ended up serving them,” implying that semantic clarity, not flexibility, is what agents reward.

For product teams, this suggests that it could be worth considering whether your product’s data structures can be read cleanly by agents.

3/ Agents are misbehaving - and tech leaders are worried

Anthropic CEO’s intervention this weekend was in part triggered by a series of AI agent incidents over the past few weeks.

Anthropic itself disclosed that infostealer malware is hijacking Claude sessions and draining tokens covertly, with victims reporting 10-100% token consumption from no personal activity, and the company acknowledging it lacks itemised usage tracking to help users detect theft even on request.

Separately, researchers found that OpenAI’s internally deployed agents autonomously created roughly 18,000 posts across undisclosed message boards to bypass sandbox restrictions. The full report goes into more detail about this highly sophisticated attack and is well worth a read. Here’s one graph that stood out:

This shows that once the security exploit was confirmed, over 90% of agents on the message board participated in the Hugging Face attack, ramping up rapidly from about 20%.

How other leaders are responding

Sam Altman agrees with Dario Amodei and says OpenAI will commit to introducing independent evaluators.

Hugging Face CEO Clement Delangue said OpenAI’s public disclosure of the attack reinforced his view that the industry needs “100x more transparency” or faces “big trouble”

Elon Musk retweeted support for Dario’s idea and Demis Hassabis (Google DeepMind) also endorsed Amodei's essay as “correct for meeting this critical moment,” tying it to DeepMind's own proposal for an industry-wide AI standards body.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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It was not so long ago that Uber became a case study in the perils of tokenmaxxing, encouraging its engineers to maximise their spend so much that their CTO later admitted that they’d already burned through their AI budget in March of this year.

Since then, the tide has turned aggressively away from tokenmaxxing, with companies now flexing their ability to deploy tactics that reduce wasteful token spend.

Uber’s CTO recently revealed that the company had designed a new sophisticated cost saving model which spans across the entire product development process, including developer tooling, 1,000+ MCP servers and third party SaaS integrations.

Their CEO was understandably very keen to flex their cost savings:

The result of their efforts was an almost 10x increase in agentic requests from February to August with total spend staying the same and in some cases, costs fell up to 52%.

And Uber isn’t alone in this shift. This past week, Spotify’s Principal Product Manager confirmed that it has managed to reduce its Claude Code costs up to 90% and third parties like Ramp, Rippling and others are scrambling to release tools that teams can use to manage AI spend internally.

In this Deep Dive, we’re going to unpack how Uber transformed its engineering / product development processes to drastically reduce its AI token spend, together with case studies from other companies and new tools, tactics and processes you can use in your own company.

If you’ve been having conversations internally about how to manage AI token spend recently then this Deep Dive should hopefully help you to navigate those conversations with the new data, tools and examples you can use to get up to speed quickly.

Coming up:

1. How Uber transformed its product development and engineering processes to cut AI token spend massively

2. Other case studies in token cost saving from Spotify, Stripe, Granola and more

3. The latest tools you can use to monitor and reduce AI token spend in your own company

How Uber transformed engineering processes to cut AI token spend massively

Uber systematically transformed several specific parts of its engineering set up and product development process to optimize cost as much as possible.

Here’s a snapshot of some of the core capabilities of this new cost cutting set up:

As you can see, one of the core parts of Uber’s new cost-cutting development process is a personal dashboard that engineers can see at any time that explains how much has been spent - together with a total of potential cost savings and suggested improvements to implement those savings.

The 6 parts of Uber’s cost saving engineering set up and other examples from Spotify, Stripe and more

To help make sense of what Uber did, we’re going to break it down into six core parts.

1. They designed a bespoke equation to balance adoption and cost

Before even starting on any cost saving tactics, Uber built a cost equation which breaks down the various elements of AI usage - and attempts to balance AI adoption (which Uber is keen to grow) and AI cost (which it wants to shrink).

Read more

This week, OpenAI finally revealed what it’s been cooking up with its latest model, Astra. We’ll take a closer look at some of the things worth knowing about for product teams.

Plus, Superhuman launched a radically redesigned version of its mobile app that has proven to be pretty divisive, 1Password shipped a new unified login feature that can transform how users log into products, and Stripe’s PM reveals how agent fleets are shipping products faster than it previously took to schedule the meeting to plan them.

This is your Product Briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

New from the Department of Product Substack this week:

AI Agents in Practice - 50+ examples from the world’s leading companies
DoorDash’s coding agent now handles 130,000 engineering tasks a month. Starlink’s voice agent takes 15,000 calls a day. And Amplitude just watched its own AI agent push product activation rates from 6% to 9%, with zero human steering. In this Deep Dive, explore 50+ real examples of internal AI agents at companies like Square, Cursor, Replit, Gusto and Figma, complete with downloadable examples you can use yourself. (Department of Product)

How Vercel implemented DESIGNmd into its own design workflows
John Phamous, Design Engineer at Vercel, walks through how the team built their own DESIGNmd, a single public file that teaches any agent to design like Vercel, cutting known layout failures by 57% in testing.

How Sonos rebooted itself after its disastrous app launch
Sonos CEO Tom Conrad has a blunt answer when asked if customers have forgiven the company for its disastrous 2024 app redesign: “in every measurable way, it’s behind us, but it’s a never-forget kind of chapter”. (Decoder)

The art of Perfect Launch: How Reddit manages Mobile Releases
A Reddit engineer pulls back the curtain on Reddit’s mobile release engine, from the Wednesday branch cut to the Monday go/no-go call, plus the late merge rules, hotfix triggers, and blameless post-mortems that keep it all from falling apart.

How Coinbase used Code Connect to guide agents and shrink token costs
Coinbase’s design system team had a problem where agents kept hallucinating icon names and building components from scratch instead of using existing ones. Figma’s Jenny Xie breaks down exactly how Coinbase modernized hundreds of component mappings and what the evaluation revealed about agents, tokens, and context. (Figma)

New product features and innovation this week

The biggest update from this week is, of course, OpenAI’s latest model, Astra. It’s easy to get caught up in the hype of model updates and so here’s a sober view of some of the parts of the model that matter to product builders.

According to OpenAI’s benchmarks, the new model performs exceptionally well at Computer Use and is self-described as “the world’s best computer use model”. The numbers back it up. On OSWorld 2.0, OpenAI reports 72.6% accuracy at roughly 40 minutes per task, versus 65.7% at 75 minutes for GPT-5.6 Sol.

Here’s a demo of it in action competing in the Financial Modeling World Cup challenge using computer use capabilities:

You can also use Astra to build better presentations that are grounded in tailored components rather than generic AI slop. OpenAI says its their best model yet for adhering to existing templates and producing slides that follow and match your visual style.

You give it a reference file and Astra outputs decks based on those files:

OpenAI says it is now 1.9x faster at task completion than 5.6 Sol, meaning users can delegate Astra tasks that you’d rather not do. OpenAI gives the examples of apartment hunting, childcare analysis and Visa form applications as examples of the kinds of tasks you can give it.

For product teams, these new capabilities could potentially lead to new user-facing features, too. More on how you can use computer use models in feature development here.

Other examples of Astra in use

It’s still early days but to get a sense of what people a doing with the model so far, here’s a sample of some interesting use cases:

• Shopify’s designer shared an example of it generating UI components, saying it’s “really good” at it

• Codex developer Tomas Ricuard shared this example of it building 3D models of a home

• Greg Isenberg shared 9 prompts worth trying in Astra

• MagicPaths’ CEO Pietro Schirano shared this example of it to 3D render hardware designs

Google has launched Gemini Live voice features across Gmail, Docs, and Keep. Powered by Google’s Gemini 3.5 Live model, this adds the ability to have conversations with Gemini across your emails and documents. The demo is pretty impressive with Google’s voice models clearly capable of handling conversations well, but putting these new abilities aside, the search functionality inside Google’s core Gmail product is becoming increasingly difficult to use as their product teams dump more and more features into their core navigation. I hope after this initial blitz of adding AI everywhere, they’re able to take a step back and assess the overall UX on some of their core products which seem to have suffered at the altar of AI feature worship in recent times.

Amplitude has launched Agent Analytics - a new feature which scores every production agent session on task completion, response quality, friction, safety, and negative feedback out of the box. It comes with eval agents that can autonomously file Linear tickets or PRs when they detect regressions.

Superhuman released a completely redesigned Mail app, consolidating the Mail brand under the Superhuman umbrella and emphasizing speed. The redesign introduces some interesting interactions, including the ability to search by pulling anywhere in your inbox but also removes some of the interactions that Superhuman says were difficult to discover. Some users like the redesign but the changes have alienated power users who are not happy:

Replit’s mobile app, on the other hand, has gone down well, with users praising its new swipe interactions.

1Password has shipped a new universal sign in interface that makes every sign in page use the same UI if the user has the browser extension installed. The system adapts to a user’s preferred sign-in method on each site over time, so it can prioritize what you actually use rather than showing every option every time. It can also now identify login fields and submit buttons more reliably, which lets it auto-fill and auto-submit even multi-step login flows (email step, then password step, then 2FA step) rather than just filling one field and stopping.

Given 1Password’s popularity, for product teams it’s worth being aware of this new change as it could transform how users log into your product:

Perspectives from some of the world’s top product leaders

Cristina Cordova, COO, Linear - made an observation that leading AI-forward companies are moving away from buying off-the-shelf agents and instead building proprietary ones trained on internal codebases and conventions, citing Ramp’s “Inspect” agent as an example, positioning Linear as the infrastructure layer any custom agent plugs into.

Boris Cherny, Claude Code lead, Anthropic - argues that background computer use where Claude operates a user’s machine autonomously via Claude Cowork and Claude Code, is an underappreciated capability that isn’t getting enough product attention.

Greg Brockman, OpenAI’s President and Co-Founder, outlined his vision for where Astra will have the greatest impact beyond its benchmark-leading capabilities. He highlighted entrepreneurship, scientific discovery, and enabling small teams to tackle large problems as areas where the model’s capabilities are most transformative.

Jeff Weinstein, Senior PM at Stripe, says small teams with “agent fleets” are now shipping new products in the time it used to take just to schedule a kickoff meeting, a pattern he’s observing across startups and some larger-company teams. He also flags that consumer-facing agents are hitting functional maturity (citing an agent that remembered a restaurant booking, modified it, and confirmed the change without human help) but argues most businesses aren’t operationally ready for agent-driven transactions at scale.

Anton Osika, CEO, Lovable, argues that the era of AI model selection is coming to an end. He says Lovable’s platform will move toward automatic model routing based on 1B+ prompts of learning data, rather than requiring users to pick models. The feature learns which models, tools, and instructions work best for each task and handles retries and replanning automatically.

📈 Product data

OpenAI’s ad business hit a $1B annualized run rate roughly 200 days after launch and is now live in 40+ countries.

Datadog says 70%+ of organizations now run three or more LLM models concurrently, and the share running six or more nearly doubled year over year. Multi-model deployment is now the operating assumption, not the edge case.

GPT-6 Astra scored 41.4% on Zapier’s AutomationBench, the first model to clear the 40% threshold, versus a prior high of 28.8% from GPT-5.6-Sol. AutomationBench scores AI agents on end-to-end completion of realistic, cross-application business workflows (Sales, Marketing, Operations, Support, Finance, HR) where the agent has to discover the right REST API endpoints itself, follow policy documents, and avoid potential distractors.

Stripe’s Tempo stablecoin product crossed $1 billion in 30-day transfer volume.

Salesforce says Agentforce + Data 360 combined ARR hit $3.9B in a single quarter, with MCP call volume up 6x and customer attrition near an all-time low during the transition. This suggests agentic products are consolidating into a single unit of customer value rather than cannibalizing existing revenue.

Uber is cutting 3,300 jobs, around 10% of its global headcount, including a 20% cut to manager roles and restricting remote work to under 1% of staff.

Google’s agentic video understanding for Gemini cuts token consumption up to 88% and cost up to 66% on video analysis while improving accuracy up to 7%, particularly on long-form content.

🔮 Emerging trends to be aware of

1/ Shadow AI usage is continuing to grow

81% of mid-market leaders (500-4,999 employees) report shadow AI use inside their companies despite official governance policies.

Retool’s survey of 307 CTOs, CIOs, and CISOs found only 5% were very confident they had full visibility into AI-generated tools running in production, and that ten agents alone create 45 possible connections before external databases and APIs are even counted.

2/ Model-provider allegiance is becoming a strategic liability

After its takeover by SpaceX, OpenAI has formally confirmed that it plans to end support for its models in Cursor. Replit’s CEO used the moment to pitch itself as the “independent, multi-model alternative,” even offering to fund customer transitions.

For product teams and leaders, the termination is an insight into the pitfalls of relying on a single vendor, which makes it hardly surprising that the data shows that companies are increasingly relying on more than 3 models to power their AI efforts.

Legal tech startup Harvey’s co-founder shared a practical version of de-risking allegiance where rather than betting on one frontier lab, Harvey composes inference providers, neolabs, and eval infrastructure from multiple specialized vendors so no single provider can become a point of failure.

3/ Physical-world robotics for infrastructure operations is moving from pilot to deployment

Meta is testing robots designed for cable-swapping and server resets in data centers, with one estimate suggesting a working cable-swap robot could replace up to 80% of some workers’ current tasks. This sits alongside Uber’s London robotaxi launch with Wayve and Zoox’s airport expansion in Las Vegas, suggesting physical automation is no longer confined to warehouses and roads, it is reaching into the data centers that power AI itself.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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DoorDash’s coding agent now automates over 130,000 engineering tasks a month, Starlink’s voice agent deals with 15,000 incoming calls and Amplitude’s CEO said this week that thanks to suggestions from its internal agent, product activation rates jumped from 6% to 9%.

These might sound like vanity metrics in some cases but even if we take them with a pinch of PR-infused salt, the quantitative benefits of new internal agents are definitely starting to emerge.

Asana says its AI team mate led to $100,000 in savings, Ramp’s router cut token spend by 30% and Deel says that its internal payroll agent transformed a process that used to take 45 minutes into just 10 seconds.

It’s been over a year since we last took an in-depth look at AI Agents in practice across multiple categories and based on these examples alone, it’s fair to say that since our last analysis, internal AI agents have moved beyond the experimental phase into scale up mode.

As we return from the summer break again, this new Deep Dive is designed to bring you up to speed with how some of the world’s leading companies are deploying AI agents internally in 2026.

Last year’s analysis looked at 25 different internal AI Agents in practice and for this latest edition it’s doubled to over 50 as more companies use internal AI Agents in all sorts of different, powerful ways.

How this Deep Dive is structured

The Deep Dive includes over 50 different new examples of internal AI Agents in practice from some of the world’s leading tech companies including Stripe, GitHub, Notion, Replit, Atlassian and more.

Here’s a preview of some of the internal AI agent examples included:

Upgrade to unlock 50+ agent examples

The full report is available to download in HTML, csv and markdown format so that you can not only read about agents in practice but actually do something with them; for example, you could upload the markdown into Claude, Gemini or ChatGPT and ask for suggestions on how you might be able to build similar agents in your own product. Sample prompts with ideas on how to do this are included.

Each agent example is broken down into:

1. Company - the name of the company

2. Agent / feature - name / explanation of the AI agent used internally

3. Category - more on that below

4. What it does - a more detailed description of what the agent actually does, summarized from a product perspective

5. Reported impact - where possible, a summary of what this agent actually impacted after being shipped internally

The report spans agent examples across both B2B and B2C product teams and isn’t limited to just engineering.

Agent categories include:

1. Product and Design - agents are now directly involved in the product development process outside of software engineering. The latest crop of agents can dig through user behavior, feedback, and session data to spot problems or opportunities a team might otherwise miss and more.

2. Engineering / Software Development - agents writing and reviewing code with a human still signing off before it ships. Snapchat, for example, built Casper so engineers can just ask for help in Slack, watch it dig through tickets and code, and open a pull request, all using only the permissions of whoever asked.

3. Finance, Payroll and Compliance - agents built to handle finance and compliance, reviewing spend, approving what fits the rules and handing the rest over to a real human. Deel’s Akai for example, catches payroll sync failures, tries fixing them on its own, and when it can’t, pulls together the context a human needs.

4. Customer Support / Sales - agents that are taking live calls and chats. SpaceX has Grok Voice picking up Starlink support calls, walking customers through hardware troubleshooting, and finishing order fulfillment, handling over 15,000 calls a day.

5. Security - agents are increasingly being used across various aspects of security. Ramp, for example, let AI agents loose on its own codebase with no specific direction, and they found, validated, and patched around 100 vulnerabilities over six days without a single human writing a fix. Other companies are turning to agents to identify security issues too.

6. Platform & Governance Infrastructure - agents that decide who gets access to what, how agents find the right tool, how everything stays auditable.

A closer look at the agents from each category

Now let’s take a more detailed look at some of the agent examples featured in the Deep Dive, starting with new internal agents built to help teams during the product and design process.

Read more

This week saw the continued adoption of agentic payments with some interesting new features and examples of internal use cases from Coinbase, Stripe and others. We’ll take a closer look at some of the ones that matter.

Plus, after Meta’s historic $18 billion settlement for addictive features, other companies roll out teen safety features and new data from Twilio shows that despite everyone’s best efforts, almost 80% of users still aren’t fully convinced by AI agents in customer support.

This is your Product Briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

New on the Department of Product Substack this week:

How to use Computer Use AI abilities when building new features
Vanta’s CEO called it “one of our biggest requests”: an agent that visits a vendor’s website, takes screenshots, and builds a full compliance audit. It’s a glimpse of what Computer Use AI can now do inside real products, not just chatbots. Learn more about how Claude, Gemini and ChatGPT’s computer use abilities actually work, how they’re benchmarked, and where Linear, Google and Vanta are already shipping features with them. (Department of Product)

How Booking.com built “talk to your data”
In the traditional stack, the “interface” to data was either a dashboard or a SQL editor. Kostiantyn Okhrimenko at Booking.com Engineering breaks down how their team built real time data analysis by pairing Snowflake semantic layers with AI agents, letting anyone query data in plain English while keeping answers governed and trustworthy. (Booking)

Six principles for mastering Answer Engine Optimization (AEO)
Despite Reddit dominating search rankings, the internet’s most-cited search result gets rejected by LLMs as a source over 90% of the time. Ahref’s CMO shares six principles for answer engine optimization. (X)

How do you actually measure the impact of AI on design?
Figma’s design research team built their own AI tools to run 639 interviews, code 2 million words, and track three years of sentiment data on how AI is reshaping design work. The result is Figma’s 2026 AI report, which found collaboration impact nearly doubled in a single year. (Figma)

Why traditional product ceremonies still matter - a case study from Atlassian
Atlassian documented how they built a weekend prototype into a production system in five months at 5x normal output velocity. But they learned that traditional product ceremonies still matter - even with AI agent team mates. (Atlassian)

New product features and innovation this week

Coinbase built an internal Slack bot where agents autonomously execute micropayments against a marketplace of 10,000+ services to answer employee questions, eliminating approval workflows entirely for small purchases - an early example of agent-to-agent commerce inside a large company that could change how the procurement process works in the future.

You can read more about agentic payments and the x402 payment protocol explained for product teams in this Knowledge Series. More on this topic later.

Google has launched the ability to load purchased ebooks directly into Gemini Notebook for grounded Q&A, summaries, quizzes and audio overviews, launching with 100,000+ books and featured Notebooks from authors like Steven Pinker and Michael Pollan. Users must own a book to access it in a shared notebook, tying Gemini’s value directly to Play Books purchases.

Product-oriented books like the Lean Startup and Thinking in Bets were put front and center of Google’s promo materials, suggesting that product teams are a core user group for the feature. Annie Duke, the author of the classic Thinking in Bets, celebrated the news but some authors may be put off by the idea of users summarising or using their works in ways that hadn’t necessarily been intended. For Google though, it’s a neat way for users to expand Notebook capabilities.

Google has also confirmed it is automatically expanding AI Overview summaries at the top of search results for some queries, pushing the typical list of organic links farther down the page. The change affects the layout and visibility of traditional search results which could impact SEO.

Amplitude added cookie-consent gating to web experiments so tests can run flicker-free while consent decisions are pending, addressing a real compliance gap for GDPR/CCPA-regulated experimentation.

Snapchat’s Lens Studio enabled a live flight-tracking AR experience at Heathrow Airport, where planes land, take off, and park based on real-time public flight data with interactive information cards - a move into data-driven AR beyond entertainment.

Uber introduced live video streaming for parents to monitor teen riders during trips, using the driver’s selfie camera. Both driver and teen are notified when a parent views the stream, and video is deleted after the ride ends. It’s already live in nine markets, with nationwide US rollout expected in coming weeks. Following this week’s ruling against Meta which forces them to pay billions of dollars over a 10 year period for building deliberately addictive features, it’s reasonable to expect to see more teen safety focused features released in the coming months.

Hex launched Generative Chart Controls, a point-and-click panel for styling agent-generated charts without re-prompting, with all changes synced back to underlying code.

Replit has launched intelligent model routing that automatically matches each task to the best model for it, balancing quality, speed, and cost. In testing, Replit says it matched previous output quality at 65% lower cost.

Perspectives and opinions from some of the world’s top product leaders

Nan Yu, Head of Product, Linear - rejects the idea that SaaS products should go fully headless to let agents work independently, arguing mature products need both embedded agents and strong MCP integration hooks because the user base is genuinely split between agent-native and integration-first preferences. This feels like a direct challenge to the “headless is the future” argument circulating in product circles but in stressing that both approaches are needed, he still implicitly supports the wider view that product teams need to be thinking about multi-surface UX.

David Cramer, Co-Founder and CTO, Sentry - Cramer says executives across every function need hands-on, sustained experience building with LLMs to make sound decisions, and that outsourcing that understanding to consultants fails the same way it has for every prior technology shift.

GitHub’s CPO, Mario Rodriguez, described a deliberate shift from UX to AX (agent experience) design, citing the Copilot app’s new Azure DevOps support as evidence that integrations are being built machine-first. “Next, we’re turning more integrations from UX into AX. Designed for agents, not just humans. That shift has me fired up” he says.

Eric Glyman, Co-Founder and Co-CEO, Ramp - frames all AI ROI as reducible to one metric (return on spend) and points to Ramp’s Router tool letting customers cut AI costs 40% for the same output by picking the right model per request.

Lovable’s co-founder, Fabian Hedin, argues that SaaS consolidation will reduce direct user interaction with individual tools, but the underlying capabilities remain valuable. He warns vertical SaaS builders to design for AI consumption of their APIs and functionality, not just human users, positioning this as the larger growth opportunity ahead.

📈 Product data to stay informed

New research from Twilio shows a massive 78% of users have tried to bypass AI Agents and speak to a human instead during customer support user journeys:

Ramp’s internal coding agent, Inspect, now handles 75% of merged pull requests at the company in under a year, running “multiplayer by default” with no opt-out.

Meta explored cutting up to 60% of many teams in two waves to go “AI-native,” then reversed the decision after staff resistance and internal evidence emerged that agents weren’t performing as expected.

Atlassian said its 5 person product team ships 5x faster with AI - with some important caveats.

DoorDash built a multi-agent system that turned a 1-2 hour manual task (removing stale feature flags across 60,000+ flags and 623 repositories) into a 13.8-minute, $4.79 automated process with a 90% success rate, deliberately designed to fail safe by prioritizing “completeness over correctness.”

Amazon also released SOP-Bench, an open benchmark of 2,000+ real enterprise procedures across twelve business domains. With eleven frontier models tested, they found that newer models don’t consistently perform better, and no single model-agent combination wins across all procedures, meaning task-specific evaluation before production deployment matters more than general model leaderboards.

LinkedIn crossed 1 million uses of its “seems like AI slop” reporting tool for AI-generated posts, and began surfacing that feedback directly to the creators of flagged posts.

OpenAI has now seen 13+ executive departures in 2026, including its CRO, COO, ethics head, and most recently data center lead Chris Malone, whose responsibilities were split across three executives reporting up through newly-expanded President Greg Brockman.

🔮New emerging trends to watch

1/ Payments and commerce are becoming agent-native

Grok Bot can now pay for things online following a new integration with Stripe. Stripe’s CEO Patrick Collison said “if you haven’t tried Grok Bot, you should. It’s very good”. The new release is part of a wider trend where payments and commerce are becoming agent-native.

Amazon (AWS) launched AgentCore Payments to general availability, integrating with Coinbase and Stripe to let agents pay for APIs, content, data feeds, and even other agents, with built-in spending limits and observability.

Robinhood’s agentic trading feature surpassed 100,000 customer accounts in its early weeks, with users trading millions of dollars daily in equities and options.

Stripe’s Senior Product Manager Jeff Weinstein argues pay-per-use pricing with upgrade paths will become the standard model for agent-accessed services, with Stripe providing the identity and payment infrastructure underneath.

2/ Internal tools are becoming productized

Ramp built Inspect and Router to solve its own engineering and finance cost problems before productizing them, and its co-CEO explicitly frames the pattern as how the roadmap gets set.

Cloudflare built an internal tool called EmDash - a new open source CMS that it describes as the “spiritual successor to WordPress”. The CMS is now more widely available for product teams to use too.

Gusto built a unified, self-service document extraction platform and then productized it into a customer-facing offering.

3/ Pricing is compressing aggressively

OpenAI cut GPT-5.6 Sol API and credit pricing more than 20% for three months. Cohere launched a document processor at $1.50 per 1,000 pages, claiming a 95% discount versus frontier LLMs and hyperscalers.

Vercel is giving away a finance-specialized model (Ling 3.0 Flash Fin, 256K context) free through to September 25th. Glean claims its new Intelligence layer costs $0.58 per query versus $2.98 for Claude Cowork, an 81% reduction.

Ramp’s co-CEO says its model router cuts AI spend 40% for the same output by picking the right model per request and Anthropic has confirmed a permanent 25% discount on all its subscription plans.

4/ Voice and multimodal agents are shifting to a new accelerated adoption phase

SpaceXAI’s Grok Voice is processing over 15,000 inbound Starlink support and sales calls daily, diagnosing hardware issues and fulfilling 3,000+ orders weekly across voice and chat.

Google’s Gemini Live added a Spark integration for multi-step voice-triggered tasks across Docs, Sheets, Drive, and Gmail, plus a Daily Brief and hands-free inbox management.

Stripe has adopted ElevenLabs’ voice agent and creative tools internally for both customer interaction and marketing.

5/ Verification and human-in-the-loop review are emerging as essential parts of AI driven development

Putting humans in the loop is becoming an essential part of agentic workflows for security and other reasons.

Atlassian’s Teamwork Lab published research finding reviewers are 22% less likely to catch flaws in AI-polished drafts than in unpolished text, and identified a five-second intervention that mitigates the effect.

WorkOS documented that attackers now deliberately craft prompts to trigger “approval fatigue,” getting reviewers to rubber-stamp requests without reading them - an adversarial pattern that exploits human attention limits rather than technical gaps.

But, human in the loop checks doesn’t necessarily stop bad code from entering production…

Salesforce documented a case where an AI coding agent misread an ambiguous requirement at the start of implementation. Every downstream decision remained internally consistent, tests passed, and a human reviewer approved the diff, despite the foundational misunderstanding being invisible to all of it.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

Subscribe now

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Compliance startup Vanta’s CEO recently announced the launch of a new Computer Use feature in response to what she describes as “one of our biggest requests”:

The company’s product teams found that after analysing user behavior, users were manually uploading screenshots to their agent of third party vendors, something that their official API didn’t support.

Now, the new computer use powered agent lets users give it a URL of a vendor to check and the agent will handle the rest:

The Vanta agent asks for permission from the user, spins up a live browser to visit the company website, captures screenshots and records a full audit of that company that can then be used as part of their compliance assessment.

For product teams, this is a neat example of how the latest AI models can be used to build powerful new features and workflows that solve real problems.

In recent months, computer use abilities have continued to rapidly evolve; Claude desktop now has computer use capabilities, Google Deepmind shipped computer use in Gemini Flash in June and last month, OpenAI revealed computer use capabilities for ChatGPT on desktop too.

Many of these are frontier AI companies shipping computer use functionality designed to be used inside their own tools but as we can see with the recent Vanta example, product teams at SaaS companies are also now starting to experiment with integrating these abilities into core products to ship powerful new workflows that plug the gaps not served by APIs.

In this Knowledge Series, we’ll take a closer look at the latest computer use abilities, how they work, the key terminology worth knowing, together with some real world examples from leading companies.

Coming up:

• Computer use explained - how does it work and what can the latest models actually do?

• Real world applications of computer use in products - practical examples from Google, Linear and others in more depth

• Opportunities for product teams to consider - ideas and frameworks for how and when product teams can incorporate these new technologies to build powerful new features

Computer Use Abilities Explained

“Computer use” is a broad term that describes a system or agent that can see a user’s screen (usually through screenshots) and act upon it in the same way a user might do: clicking, typing, scrolling, and navigating.

This has both its upsides and downsides which we’ll come onto later, but the ability to do this inside products through computer use APIs has started to spur product teams to embed some creative new workflows into their products.

A snapshot of where we’re at right now

All of the major AI frontier companies now offer computer use capabilities.

Anthropic added computer use to Claude as a research preview back in March this year, letting users open files and navigate autonomously which was later integrated with Dispatch.

Google shipped computer use in Gemini Flash in June which lets the model see and interact with screens, together with API access through the Gemini API.

And OpenAI recently added computer use to its newly refreshed ChatGPT desktop app on MacOS and Windows in July, with a Chrome integration and multi-step tasks.

Since it’s release, OpenAI flexed its ability to cope with longer, complex workflows including this example from an OpenAI engineer of processing seven years of tax returns and immigration documents to complete an immigration package application:

How are Computer Use abilities benchmarked and how well do the models perform?

The most widely used benchmark for computer use is OSWorld and more recently, OSWorld 2. The original OSWorld was centered around 369 tasks averaging ~30 steps each and the new version of the benchmark is a smaller but much deeper set.

It includes over 100 different long horizon workflows, requiring an average of 318 tools which would take a human around 1.6 hours to complete.

Read more

This week, Slack revealed its latest update that positions AI coding agents as co-workers. Investors are impressed, but what does it actually do?

Plus, one of Anthropic’s lead engineers argues that product teams are leaving a lot of money on the table by not making their product headless and available to agents. We’ll take a look at MCP releases worth knowing about. And should leaders ship more code? Doordash’s CPO reveals what he thinks.

This is your product briefing. Have a great week ahead!

Rich

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Key reads and resources for product teams

New from the Department of Product Substack this week:

Linear’s CPO says MCP usage is “through the roof” - Six Emerging UX and design trends explored
The number of Linear issues created by agents have now overtaken those created by humans. If your users are a mix of humans and agents, how should that change design and strategy? In this week’s Deep Dive, I explore six emerging UX trends reshaping product design, with real examples from Linear, Netflix, Runway, Square and more. (Department of Product)

How PostHog builts its own semantic data layer for agentic analytics
Ask three different AI tools for last month’s MRR and you’ll get three different answers, each one confidently wrong in its own way. This is the problem Thiago Rocha Salvatore and Lizzie Epton set out to fix at PostHog by building a semantic layer, a governed catalog that gives agents and humans one shared definition of what your metrics actually mean. (PostHog)

A new skill that people at Anthropic are using a lot
Anthropic’s engineer shared this skill that’s used to explain concepts “like I’m someone who knows nothing about this topic, using a HTML artifact with big pictures and few words”. (Anthropic)

How to design the first five minutes of your product’s agent UX
Microsoft’s senior UX researchers share their thoughts on why sequencing builds trust in the first few minutes, which determines whether users stay engaged or abandon your agent. (Microsoft)

How to build a company Agent Skills library
This week, Notion Agents gained Skills which means agents can learn a team’s existing workflows, encode them as reusable instructions, and share or port them to local agent instances. Notion’s product marketer John Hurley shared this guide to building a company library which could come in handy if you’re thinking about building your own Skills:

New product features and innovation this week

Slack has released Slack Code - a multiplayer coding environment in Slack where agents from Anthropic, GitHub, Cognition, and Vercel work alongside humans in shared channels. While it’s previously been possible to work with coding agents through integrations in the past, In practical terms, this means that agents can now participate in a channel as a co-worker, alongside humans and when a task is assigned to an agent, a dedicated channel with the relevant agents and people is created. Here’s a demo of it in action from Slack’s VP of Product:

Slack is positioning itself as the glue that stitches agentic development workflows together - and it seems to be working. Parent company Salesforce stock is up 33% over the past month.

Atlassian has launched a new feature called Jira planner that’s designed to help product teams write up specs so that agents know what they’re supposed to be building before starting. Jira Planner starts with a rough idea, then asks clarifying questions about scope and constraints. Output goes into editable Confluence Live Docs, connected to Jira, so PMs, engineers and designers can co-edit before anything is finalized. Once ready, it splits the plan into sequenced Jira work items with acceptance criteria, then hands off to agent orchestration.

Anthropic released an early preview of a new /design command in Claude Code that generates multiple UI mockup options directly inside the Desktop or CLI interface. It then lets users pick one, edit it, and implement it without leaving.

After Anthropic confirmed that all text output is now watermarked to comply with EU regulations, one user has asked whether designs are watermarked too. Watermarking is a real issue of contention and Stratechery’s Ben Thomson revealed that he is “deeply philosophically opposed to watermarking in the context of the entire meta question about the relationship between humans and AI”.

New Headless MCP releases

Anthropic’s Thariq argues that there’s still a lot of money that product teams are leaving on the table by failing to make their product headless and letting agents use it:

Here’s some of the latest new MCP additions shipped this week:

Stripe has added an MCP connector letting agents query Stripe data directly for custom MRR, churn, and retention reports. Useful for product teams who want access to revenue-related data.

Salesforce has released Data 360 Headless - which lets agents access the full Data 360 platform through tools like Claude Code, Cursor and more.

Rippling launched MCP gateway that allows companies to monitor access controls and token usage spends.

Brave has integrated WebMCP - a new standard that lets agents read about the structure of your website. A previous Knowledge Series went deep on that here if you’re interested in learning more about WebMCP.

Perspectives and opinions from product leaders this week

Stanley Tang, Co-Founder & CPO, DoorDash - endorsed the view that engineering leaders must personally build and ship especially with AI, to avoid falling out of step with their teams, saying that hands-on shipping is core to DoorDash’s culture. For product leaders, this is a direct challenge to pure-management career tracks and reflects the wider trend of encouraging leadership teams to take time out to build with AI.

Wade Foster, Co-founder & CEO, Zapier - warned that AI systems are systematically sycophantic, sanding down ideas through repeated iteration, and proposed combating it with adversarial multi-agent setups where subagents have conflicting incentives and even “bet” simulated stakes on outcomes. He admitted this pattern shows up inside Zapier’s own workflows, a rare public acknowledgment of an internal AI limitation from a CEO.

Mikhail Parakhin, CTO, Shopify - described Shopify’s internal cycle for improving AI agents. The process includes multiple steps: optimize the ground truth, then the prompt, then the model, then distill, then repeat, treating ground-truth data as the “northstar” anchor for all downstream tuning. A potentially useful transferable framework for any team building LLM judges or agent evaluation pipelines.

Jori Lallo, Co-founder & CPO, Linear - endorsed the idea of automated Slack Connect invites triggered on signup as a pattern any B2B startup should copy, arguing it preserves momentum that an email-and-calendar loop would kill. For product teams, Linear is explicitly betting that agents, not humans, become the primary end user of an integrated dev platform.

Barry McCardel, Co-Founder & CEO, Hex - argued that the term “forward deployed engineering” is widely misused, and the real test is whether product direction originates from the field or from PMs and engineers in headquarters who rarely see customers. Relevant to any team debating where roadmap authority should sit as agent-driven, customer-facing deployments become more common.

📈 Product data and performance insights to stay informed

Non-engineers are shipping more code than ever according to a new study from Linear.

The study also found that teams using coding agents ship 6.5x more code PRs per team rose 111% platform-wide and agents now author half of all issues.

15 million US users were part of a TikTok A/B test that intentionally disabled a safety safeguard meant to prevent harmful-content overexposure. US senators Blackburn and Blumenthal have demanded answers on 13 specific questions as a result.

OpenAI says Asana cut an estimated 5 year front end migration task to just two weeks with Codex. Even if you take the 5 year estimation with a hefty dose of PR salt, the results are still pretty impressive.

Zapier’s CEO shared internal research across 1,500 companies showing that even leading AI adopters use AI in only 18% of agentic workflow steps, with the rest still running on code and logic, and argues token spend doesn’t correlate with ROI. Zapier’s own heaviest AI users spend up to $30,000 a month, mostly on coding agents doing greenfield work.

Gemini 3.7 Flash “smashed” Google’s previous Gemini growth records in its first week, making it the company’s fastest growing model yet.

Stripe’s president said that for a new feature, AI wrote 30% of code in a week and global tax filing shipped in a third of the time the US version took. After AI made sellers 20% more productive, Stripe resisted making cuts and instead hired even more sellers.

Deel cut deployment approval from a 4-hour, 60-team regression process down to 40+ daily deploys with a single human decision point, using AI agents to pre-screen every database migration for risk before humans see it, and to trace errors back to their source with drafted recovery plans.

🔮 Emerging product trends to keep an eye on

Keeping up with relevant updates is one thing - but identifying some of the underlying trends is another. Here’s some of the emerging product trends worth keeping an eye on this week:

1/ AI credit pricing models and transparency is evolving

Linear moved coding-session pricing to provider rates with no markup, adding a flat runtime fee and optional spend caps, explicitly in response to customer complaints about pricing complexity. Lovable added credit check-ins that pause long-running messages at a configurable threshold to show accrued cost before continuing, and separately made credit depletion pause work mid-message instead of stopping it outright, with resume options. ChatGPT is quietly testing a “pay-to-reset” button that resets monthly AI credits.

2/ Agent-specific product team roles are on the rise among leading tech companies

The latest job postings from tech companies show a cluster of new roles around agentic AI:

• Uber (Senior Program Manager, Agentic AI),

• Asana (Senior Engineering Manager, AI Agents)

• Airbnb (Staff PM, AI Personalization)

• Webflow (Senior PM, AI)

• Smartsheet (Principal PM, Data & AI Platforms)

• Toast (Principal Product Designer for its “Toast IQ Core” AI team, and separately a Senior Director of Engineering, Developer and Agent Experience).

3/ Video and voice predicted to be the ultimate new user interfaces

Runway’s CEO argued this week that video will become “the final interface” for all software, extending an idea that UI will converge on chat-and-gesture input with video output. He’s the CEO of a video generative AI startup so he would say that, but he was echoing what famous Uber investor Naval Ravikant originally said.

On the voice side, Grok Voice’s Think Fast 2.0 model topped a new Speech Agent Arena benchmark specifically for task completion rather than naturalness, task success. If that framing continues, we can expect expect voice-interface evaluation to shift from “does it sound human?” towards “does it complete the job?”

4/ Vertical, domain-trained models are starting to beat frontier generalists at a fraction of the cost

Product teams are starting to build their own domain-specific in-house models to help users complete complex industry tasks. This week, unicorn legal startup Harvey revealed that its Tenet model outperformed its Kimi K3 base by measurable margins on legal benchmarks, and a separate research effort using Harvey’s synthetic law-firm data trained a 27B parameter model that outperformed frontier models at 10x lower cost per query.

Superhuman has its own in-house models - and has done from the start, according to their CEO.

Databricks’ Precision Mode beat frontier models by seven points on complex document extraction using custom-trained models plus an agentic harness rather than a bigger general model.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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Linear’s CEO recently confirmed that for the first time ever, issues created through MCP officially surpassed application created ones, arguing that Linear is now a product that serves both agents and human users.

Their Chief Product Officer, Jori Lallo, put it pretty bluntly, saying that MCP usage is “through the roof”:

As agents and MCP usage continues to increase, this poses an interesting strategic question for product teams and product designers especially: if the users of your product are a hybrid of humans and agents, what does this mean for your product’s strategy and how do you approach design-led development in the future?

Linear isn’t alone in this, either, with plenty of companies shifting their strategies towards supporting a growing volume of agents.

Shopify’s Hydrogen framework is now built “agent-first”, Square now supports sales bought through Google Maps agents and HubSpot became one of the latest products to build a CLI (command line interface) designed specifically for agents.

Netlify now even offers a toggle on their homepage to flip between the human and agent experience:

The Six Emerging UX and Design Themes for product teams

Headless, agent-first design is just one of many ways traditional design thinking is being disrupted right now and in this Deep Dive, we’re going to take a closer look at this along with other UX and design trends worth knowing about.

We’ll explore what the new emerging and potentially disruptive themes are in UX / product design, together with some areas of contention where different companies and leaders have different perspectives, all grounded in real world examples from companies including Linear, Square, HubSpot, Netlify, Webflow, Figma, Miro, Runway and more.

1. Headless-first: designing for agents as users with MCP

MCP is now a firmly established protocol for companies who want to enable usage by AI agents.

Leading product-led companies like Figma, Miro, Runway, Perplexity, Docusign, Square, PayPal, WorkOS, and Lovable have all released MCP servers in recent months and some companies are now shipping version 2.0 of their MCP products. Webflow, for example, recently shipped MCP 2.0 which gives users’ agents full access to their design systems, CMS and permissions systems.

For designers who work in companies with MCP connectors, this means user experience is now extended to both agents and humans. To reflect this shift, some companies have even started to publish documentation that is specifically dedicated to serving the “agent experience”.

The traditional interface is becoming optional

In practice, this means that product teams (including designers, PMs and developers) must now work together to audit and re-think their existing user journeys to figure out which ones are suitable candidates for MCP connectors that are specifically designed for agents.

Read more

This week, Cursor unveiled its first major product since its acquisition by SpaceX. We’ll take a closer look at what it is and how people are using it for tasks including product-related work.

Plus, why Shopify’s CTO says the narrative around SEO is wrong, Gemini has cause for celebration and concern and a new study reveals what impact having an AI super user in a team has on product innovation.

Have a great week ahead!

-Rich

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Key reads and resources

New from the Department of Product Substack:

How to manage AI agents effectively
A guide for product teams and leaders on how to manage AI agents effectively. Spend management, governance, testing and safety infrastructure tactics explored. Real world examples, tools and case studies from OpenAI, AWS, Uber, Block, Atlassian, Slack and more. (Department of Product)

How Cursor’s new icons were designed over a one year period - a case study from the designer who created them
Cursor’s new icon set looks simple, but it took a full year, 600+ hand-drawn icons, two sizes, two styles, and 156 attempts at a single hamburger icon to get there. Designer Marek Minor walks through the whole process: replacing an inherited, drifting icon library, building a system for concepts with no existing set covers, and shipping it all without breaking a single reference. (Marek Minor)

An AI governance framework
Retool’s product lead for Governance & Infrastructure published a simple runtime governance framework built around three questions: who’s acting, what can they reach, and can you stay in control. It’s grounded in Retool’s own survey of 307 CTOs, CIOs, and CISOs, where only 8% rated their internal AI governance as “strong” and just 5% felt confident they could see what’s running in production. (Retool)

The future of data analytics and why AI is rewiring the role of data analysts
Databricks’ Emma Stowell, Angus Morshead and Ogo Odili on why the analysts who thrive next will be the ones asking better questions, not writing better queries. (Databricks)

How Ramp built third party product integrations that write themselves
Ramp's Shubhayan Srivastava wanted to skip the backlog of one-off connector requests, so he built an agentic system that researches a provider's API, writes and tests the integration, and ships it in minutes instead of months. (Ramp)

New product features and innovation worth knowing about this week

Cursor has launched Grok Bot as a new standalone AI agent app. Cursor and SpaceXAI released Grok Bot in beta across Mac, iOS, Windows, and Linux, described as “AI teammates you can give real work to.” Bots sign into a user’s existing tools, complete tasks end-to-end, and return only when approval is needed, remembering your preferences over time.

The response to the product so far has been overwhelmingly positive, with Pragmatic Engineer Gergely saying he uses it for things like spam filtering, making an important point about why Google seemingly decided not to build something so obviously valuable:

Cursor says that you can use it for product related tasks such as creating a “Product performance” bot that logs into your analytics tools, grabs the data you need and shares it with you once it’s done.

Here’s some other examples of how people are using Grok Bot:

• Customer outreach and lead generation

• Life admin and calendar optimization

• How to use Grok Bot as a PM

Claude Code now has cross-instance messaging. Separate Claude Code sessions on can now message each other, sharing findings and coordinating work across parallel instances. This turns Claude Code into more of an orchestration layer for multi-agent workflows rather than a single-session assistant. For product teams, this helps for scenarios where multiple agents need to work together to build or refactor large new features.

Spotify has added an AI-generated artist label. Spotify launched an “AI persona” badge identifying artists that are created or represented by AI, aimed at giving listeners a way to distinguish humans from synthetic artists. AI artists will be excluded from both editorial playlists and algorithmic suggestions. YouTube’s CEO reiterated their commitment to human-centricity, too. More on that below.

Hand picked new features you can use at work:

FullStory launched a Customer Agents product that streams real-time session context into support tools so agents see behavioral history before an inquiry lands. So, for example, instead of “what can I help you with,” the agent opens with context-aware messages like “I see the upgrade failed twice, want me to retry it on the card ending 4412?”. You can also integrate this into other third party apps like Intercom / Fin. Fullstory says one company who used it saw resolution rates hit 79%.

Google’s Ask Advisor AI agent is expanding into Ads and Analytics with a new benchmarking feature. It compares a business’s campaign performance against anonymized averages from similar businesses. In Google Analytics, you can now use the assistant to dig down deeper into automatically surfaced insights to learn more about what’s driving them (if you’ve ever struggled with GA4’s navigation like I have, you’ll appreciate this as a welcome addition!).

Intercom added an evaluation framework to Fin. The new framework includes pre-launch testing, controlled rollouts, and live conversation monitoring, aimed at giving product teams confidence before pushing agent changes live.

Linear now streams agent coding sessions as live cards in Slack, so you can watch investigation and code changes and open diffs for review without leaving a channel.

Atlassian launched Code Context, which feeds large-scale, multi-repository code understanding into its TeamworkGraph, reducing token usage and improving output quality for coding agents. As well as this, Confluence agents are now callable via @mention directly on pages, and the same agents work across Claude, Cursor, and IDEs through Atlassian’s Rovo MCP, with teams now running more than 5 million agent invocations.

OpenAI shipped the ability for Codex to delegate different sub-agents to different models, depending on the task. If it works effectively, it could have a significant impact on token spend.

Perspectives and opinions from top product leaders this week

YouTube’s CEO Neal Mohan says that human centricity is essential for the company’s AI product strategy. AI’s job, he says, is to help viewers find human perspectives and creators, not to generate or substitute for that content.

Brian Armstrong, CEO at Coinbase has argued that grafting AI onto old product development workflows is fundamentally wrong, and that “the game has changed. You need to update the way that you work.” Coinbase ran an internal experiment where teams deleted their IDEs and wrote zero lines of code for two weeks, a model Linear is now citing publicly as evidence of where agent-driven development is headed.

Sergey Brin, Co-founder, Google, has directly urged key AI staff to prioritize Gemini model development, with some teams moved from DeepMind into corporate Google as part of an internal reshuffle.

Karri Saarinen, CEO, Linear, says this is the first week in Linear’s history where MCP-created issues outnumbered application-created ones, framing it as proof that Linear is succeeding as both “destination and source for agents and humans”. Linear’s growth strategy is now explicitly agent-first and the headless model is becoming increasingly necessary as a product strategy.

Mikhail Parakhin, CTO, Shopify has pushed back on the narrative that AI is cannibalizing organic search, citing Shopify data showing AI-referred sessions grew 3x year-over-year while organic sessions still grew 12% off a larger base, arguing the overall opportunity is expanding, not contracting. A useful counter-data point for anyone budgeting SEO versus AI-discovery investment. But despite this, there’s still plenty of examples of companies who have experienced major declines in organic traffic.

📈Product data and trends to stay informed

Gemini has officially hit 1 billion monthly active users, up from 950 million in Q2, matching ChatGPT’s scale and marking Google’s 14th product to cross the threshold.

63% of Gemini users now engage via voice, the app generates over 150 million images a day, and it has more than 100 million active users on iOS, meaning Google is starting to gain distribution even outside of its own OS.

But it’s not all good news for Google…

DeepSeek has officially passed Google to become the second-largest AI provider by token volume on Vercel’s AI Gateway, capturing 25% of July volume versus Google’s 11%. The average price per token fell 13.6% month-over-month as usage shifted to cheaper models. Open-weight models more than doubled their share of gateway spend to 8.6%, the highest in the index’s history.

81% of July’s gateway tokens ran on models that didn’t exist six months earlier, and three-quarters of high-volume teams changed their model mix by 10% or more month-over-month, evidence that dynamic model routing is now a baseline operational practice, not an edge case.

DoorDash’s Flux platform now automates 130,000 engineering tasks a month. DoorDash built an internal agent platform, Flux, that runs code reviews, tests, and builds autonomously, handling more than 25,000 code reviews and 10,000-plus playbook invocations weekly, with sandboxing and permission controls to keep agents away from sensitive systems. It’s a strong internal-tooling case study for any product org thinking about standardizing agent infrastructure.

LinkedIn’s internal AI code review system handles 79,000 reviews a week across roughly 7,500 repositories, with a 63.9% acceptance rate, and most reviews now post before a human reviewer even opens the PR.

Zapier published a new study which measures task automation completion rates - and the results are actually quite surprising. Essentially, you can use reasoning settings to make cheap models outperform more expensive models. In this case, Gemini 3.6 Flash beat Fable 5 and GPT 5.6 on the Max reasoning setting at a fraction of the cost:

But, Hex published its own study arguing that more reasoning effort doesn’t always help. In Hex’s testing, Claude Opus 5 actually degraded at higher effort levels, while Claude Fable 5 was the only model where high effort didn’t backfire, and GPT-5.6 Luna offered the strongest cost-to-performance ratio.

A team’s structure impacts output more than raw AI access. A new study by Atlassian’s Teamwork Lab found teams with a single designated AI superuser were 18% more likely to produce top-scoring work than teams without one, suggesting concentrated expertise beats broad but shallow AI usage. The study ranked teams with superusers across various product-related performance metrics like innovation, customer impact and project completion rates and here are the results:

Adding just one AI superuser boosts a team’s innovation rates by a significant 19%.

The Briefing is your curated report on what happened in tech and AI this week - hand crafted for product teams. Paid subscribers get the full DoP Substack including: The Knowledge Series for hands-on AI tutorials and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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After Amazon’s employees recently admitted that they mistakenly spent over $1.8 million on an AI feature that used the wrong model, figuring out how to manage what AI agents are doing in production is becoming increasingly important for product teams.

And in recent weeks, the market has shifted to reflect the need for companies to manage the agents they build with dedicated new products and features. LaunchDarkly launched Agent Control, Microsoft is building “agentic observability” tooling for its cloud platforms, Notion added admin controls to govern agents and Vanta revealed an open source framework called Agentic Trust Controls to set the standards in AI agent governance.

If we look back at where things stood even just six months ago, many product execs were still at the stage of identifying opportunities for AI agent involvement in both user-facing features and internal tools. But now, many leading companies have AI Agents up and running across their tech stack.

This has shifted the focus away from figuring out what agents can do to overseeing what they’re actually doing. This can be pretty daunting for product teams and execs who want to ensure agents don’t go rogue or take actions they’re not supposed to.

In this Knowledge Series, we’re going to explore how you can manage AI Agents across important areas like spend management, governance, testing and safety with real world examples and new tools / frameworks you can use from companies including OpenAI, AWS, Block, Atlassian and more.

If you’re curious about some of the new, emerging technologies, tools and terminology in this space and you’d like to know how they might apply to your own setup, this Knowledge Series should help.

Coming up:

1. What is AI agent management?

2. The 5 core areas of AI agent management explored, including: spend management, governance and security, identity and access controls, testing and safety and observability and monitoring.

3. 20+ real world examples, case studies and tools you can use to monitor spend, implement governance and manage AI agent identities from the world’s leading companies.

What is AI agent management and why does it matter to product teams?

AI Agent management is the operational layer for everything that relates to any agents a company builds. It’s everything a company does to control, observe, and govern an agent once it’s actually running against real users and real systems, as opposed to just designing or prompting the agent in the first place.

Here’s a snapshot of the 5 areas of AI Agent Management that product teams and tech leaders are currently grappling with:

Now that AI agents are working across internal and external products, the conversation is starting to shift away from ideating opportunities for agents and towards answering questions like:

• How much is this agent actually costing us per task, per user, per week?

• Does the agent get its own identity, or does it just inherit the permissions of whoever’s using it?

• Do we need sign off from legal or compliance before an agent goes live, and what does that review actually check?

The 5 core areas of AI Agent management explored

Let’s take a closer look at each of these 5 core areas in more detail.

For each area, we’ll explore some of the new, emerging concepts and terminology worth knowing about - along with real world practical examples of companies who are implementing some of these concepts.

We’ll cover:

1. Spend management

2. Governance and security

3. Identity and access controls

4. Testing and safety

5. Observability and monitoring

1. Spend management

After Uber’s CTO admitted they’d pretty much spent their entire 2026 budget and Amazon’s recent mishap, AI cost spend is top of the agenda for many product leaders right now.

And since this is all moving so quickly, one of the first problems teams run into is understanding what cost control mechanisms actually exist.

Based on what’s available at the moment, managing AI Agent spend typically falls into one of these tactics:

Gateway level budget controls

This is where you monitor and control spend at the infrastructure level; you put a gateway between your agents and the model providers and then set hard limits at that point.

For example, Databricks recently added a new feature called AI Spend Controls to its Unity AI Gateway that lets teams set budget limits and thresholds on both third party API calls and internal model usage.

Vercel has taken a similar approach with its new AI gateway, allowing users to set team and project level budget alerts.

The idea here is that the gateway is the one place all agent traffic has to pass through making it a natural place to enforce a budget limit.

Model / request routing to cut costs

Model routing is where you match the model tier to the actual difficulty of the task, ultimately saving money by not wasting spend on expensive models (something Amazon learned the hard way).

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After Stripe unveiled its internal agentic coding suite called Minions earlier this year, it has since gone on to spur several product development teams to build agent coding fleets of their own.

Inspired by the results they saw with Minions, Stripe has now revealed a new follow up internal AI tool which has been designed from the ground up to explicitly target non-engineers. And according to a Senior PM at the company, this new tool is something he uses “all day long”:

And its not just Stripe who are refocusing their internal AI tooling efforts towards non-engineers. Uber’s CTO, for example, recently confirmed that they’re now deploying small groups of engineers (what he calls “Agentic Pods”) throughout their company to work with non-technical parts of the company to understand use cases and workflows - and then build custom-built tools around them.

In this Deep Dive, we’re going to unpack Stripe’s new Knowledge AI Platform (that it internally calls “Kai”) to understand what it does, the core technologies and guiding principles involved - and how you might be able to take some of these principles to build your own version of an internal AI knowledge tool that can be used for non-engineers.

As well as this, we’ll also cover other recent examples of similar AI internal tools and agents that were recently released from companies including Spotify, Uber, Intercom and more.

Coming up

• What is Stripe’s Internal AI Knowledge Platform and how does it work? The core parts of the application unpacked

• How you can build your own version of something similar - sample prompts, guidelines and workflows

• Other examples of innovative new internal tools at world leading tech companies including Block, Asana, Uber, Rippling and more.

What is Stripe’s internal Knowledge AI Platform?

Here’s a snapshot of what Stripe’s new internal Knowledge AI platform is and how it works:

As you can see, the Knowledge AI platform is pretty expansive with its core capabilities surfaced across a number of different touch points including:

• a web app

• Slack integration

• other embedded internal tools

• and even a Chrome extension which is a super smart way to allow users to use it wherever they are.

This design means that one user might use the web interface to start an in-depth conversation (long context windows are baked in with one user reaching 932 turns) and another use might rely on the Chrome extension to plug into an existing internal tool without using the web UI very much at all.

Stripe says that this was a core architectural decision to make the tool as “surface-agnostic” as possible. The agent is built as a service rather than a single application and users can interact with it through any of its supported surfaces, though Stripe admits that the most common view is through the dedicated web app.

A closer look at what exactly it can do and the technologies powering it

Stripe says that the platform is used across many different departments within the company - and has shared some of the use cases and core achievements across them:

• Sales Research - researching accounts before calls, generating 2x sales activity, creating 17% more opportunities

• Data Analysis - 5,000+ daily sessions are centered on this alone - querying data warehouses, analyzing logs, surfacing metrics

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This week, it emerged that Amazon mistakenly spent $1.8 million on Anthropic models for a feature that ran 860% over its allocated budget. Needless to say that managing token spend is becoming increasingly important for tech companies. We’ll take a closer look at some new tools you can use to do it.

Plus, Spotify gets new context-aware UI patterns, Replit enters the design space and the war on AI slop continues.

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Key reads and resources for product teams

New from the Department of Product Substack this week:

How Netflix built a generative AI powered homepage that boosts engagement
Netflix built GenPage, an AI system that builds your entire homepage in one pass instead of separate components that know nothing about each other. The result is a page that’s 20% faster to load and measurably higher engagement. Learn more about how Netflix built it, the four core technologies powering it, and 15+ case studies from Spotify, DoorDash, and LinkedIn showing how you can apply similar AI personalization to your own product. (Department of Product)

How Airbnb involves PMs in the evals process before shipping new AI features
In a new post on the Airbnb Tech Blog, Rohit Girme and team reveal how they treat evaluation as core discipline by combining programmatic checks, AI judges, and human reviews by PMs to catch real failure modes before they reach users. Learn their five-principle framework for building trustworthy GenAI products at scale. (Airbnb)

Linear’s CPO shares the most common loop written by product teams
Linear’s Chief Product Officer explains what the most common loop created in the Linear app is - and why it misses bugs 30% of the time. (X)

Are Lean Startup principles still relevant? Stripe’s CEO Patrick Collison shares his views
In this conversion with Y Combinator, Collison suggests that while iterative building remains important, the ease of spinning up organizations with AI means founders can now pursue more ambitious, divergent ideas from day one. (Y Combinator)

9 downloadable AI skills you can use across the different layers of product design
Created by Jamie Mill, these tool-agnostic skills install once and run in any AI environment. You pick a layer, run a skill, get structured guidance. (Layers by Jamie Mill)

New product features you can use at work

Chrome now has a Gemini Spark integration. With your permission, it can access your logged in accounts and saved passwords to complete tasks on your behalf. Google calls these “errands” and for tech workers, this could include things we’d rather not do like procurement or checking up on a competitor homepage. OpenAI recently binned its Atlas browser and so it looks like we’re very much still in the age of Chrome browser supremacy.

Miro has launched ‘Talktrack 2.0’, a new update to its async video tool and rival to Loom. The updates let you edit layers, add multiple product team members into the same talk track and AI features that will build diagrams directly from the Talk track you recorded.

GitHub launched “Canvas” which lets you turn AI into interactive workspaces where you can collaborate with agents on shared visuals. Product teams can use this for things like creating swipeable cards to track issues and creating architectural diagrams:

Linear’s mobile app now supports full code reviews so your engineering teams can monitor reviews on the go.

Emerging trends that matter to product teams this week

Here are some of the emerging trends from across the industry this week based on what’s been shipped and new product market signals.

1/ AI spending control tools are accelerating after Amazon’s “catastrophically expensive” mistake

Amazon staff revealed that the company wasted $1.8 million by using Anthropic’s Claude Sonnet model to power a feature that matches author details with product listings, which equates to an 860% overspend vs its allocated budget. You could reasonably argue that $1.8 million is a drop in the ocean by Amazon’s standards but the company is well known for its cost-saving culture and one engineer told the FT that it was “difficult to figure out” how much AI features are costing.

Amazon is unlikely to be the only company to have suffered a spending blunder like this and the market is responding accordingly. This week saw the release of a bunch of new features designed to make it easier to track AI spend:

• Ramp has launched token spend management - a new tool designed to help companies track and manage AI spend. It unifies cost data from all major AI vendors into a single dashboard.

• Vercel shipped budget controls in its AI gateway, which can be scoped to both the team and project level.

• Databricks added a spend control feature to its Unity AI Gateway which covers third party API calls and internal model usage.

• OpenAI released Admin APIs for ChatGPT work which give admins a full view of vendor spend and usage limits.

2/ Authenticity is becoming a design/content signal as the war on AI slop continues

The war on AI generated slop is continuing - and product teams are shipping features that prioritise authenticity over AI content.

Following the launch of Substack’s AI detection tool a few weeks back, Snapchat has now confirmed it is changing its algorithm to favor human made content over other formats in a post entitled “Rewarding Authentic Creativity”. This week, LinkedIn followed suit with a new user reporting tool that lets users flag posts that “seem like AI slop”.

OpenAI is also filtering ChatGPT to block direct copying of named authors’ styles which is an indirect way to block users from creating content that looks and feels like another human (in this case an author).

3/ Context-aware UI patterns are spreading

Spotify shipped a running mode which changes the core UI when users are running to make it easier to use hands-free and a User Notes feature which lets people caption individual tracks with personal context. Both features are examples of designing for specific micro-contexts as products learn to become more aware of a user’s current state.

Other companies are adopting similar patterns, too. Snap has introduced a Now Playing feature that lets users share what they’re currently listening to with friends.

4/ Design to code is continuing to collapse into a single workflow

The traditional handoff process designers took for granted is collapsing and this week saw some new releases continue that trend. Replit has launched Replit Design - a new creative suite that sits inside its core product. Replit Design lets non-designers create visual work directly inside Replit’s dev platform, using what the company calls “Ambient Intelligence” to guide design decisions, reducing export/rebuild steps between design and code.

Figma moved in the same direction, albeit from the other end of the workflow, as Figma Make gained a properties panel and in-context annotation prompts to Figma Make so designers can visually edit elements and steer code generation without leaving the canvas.

📈 Product data and insights

PostHog says its shipping velocity has tripled from 1441 pull requests in January to 4725 a month in June (a 3.3x increase in six months). They’re now tracking towards 10,000 pull requests a month - and code quality is not decreasing. They also revealed that agents now create 70% of code.

ChatGPT engagement is broadening, not just growing.

The return to the office trend is far from over. Patreon is the latest company to increase its return to office numbers. This week, it confirmed that it is increasing its RTO requirement from two to three days a week and is laying off 20% of its staff.

Google’s AI Overviews now make up 43% of all searches and Google’s Chrome says it is now shifting to a twice-weekly release cycle after AI agents discovered more security bugs in the last two releases than the previous 23 releases combined:

The surge in AI powered bug reporting has forced Apple to limit the number of bug reports researchers can submit to it.

Figma says almost half (47%) of product teams report experiencing some form of AI adoption mismatch where teams and leaders aren’t adopting AI at the same speed. 20% say individual contributors are pulling ahead without organizational support, while 27% say leadership is pushing AI adoption while teams struggle to keep up:

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The homepage is the first thing users see when they visit your product. And Netflix recently revealed how it plans to fundamentally change theirs using LLMs to boost engagement.

They call this “GenPage” and it’s a glimpse into the future of how all product homepages and personalization features could work.

LLMs have proven their ability to generate responses to prompts and so the teams at Netflix decided to take the principles of how LLMs work and build a deeply personalized homepage from scratch that answers this question:

Given everything we know about this user and this request, what homepage should we generate to maximize user satisfaction?

In this Deep Dive, we’ll take a closer look at what exactly Netflix built, the underlying technologies behind it and how you might be able to use these principles and technologies to add deeply personalized features to your own product.

As well as this, we’ll explore 15+ similar examples of generative AI powered features that drive engagement from other companies including Spotify, DoorDash, Uber, LinkedIn, Instacart and others that you can use as case studies for your own product’s personalization and engagement efforts.

Coming up:

• What Netflix built and how it works

• The underlying technologies behind it explained simply

• Opportunities for introducing generative AI personalization in your product

• The full selection of 15+ AI personalization examples and case studies from Airbnb, Apple, LinkedIn, Slack and more

What Netflix built and how you can apply similar AI personalization to your product

GenPage is an experimental new technology that generates a personalized version of the homepage using a single model:

Before GenPage, Netflix built the homepage using a a multi-stage pipeline. This meant different parts of the homepage were built separately. So, for example, the homepage was split into rows and different systems would generate different rows but they wouldn’t know about each other.

You can think of it almost like an assembly line with separate workers, none of whom talk to each other:

• Worker 1 decides which rows should exist on your homepage (should we show “Continue Watching”? “Korean TV Shows”? “Because You Watched X”?)

• Worker 2 picks which specific titles go inside each of those rows

• Worker 3 decides the final order everything appears on the page

The net effect of this is that you could end up with a homepage where each individual row looks fine in isolation but the overall UX is poor. This is something that product teams have traditionally struggled with in recent times - particularly since the shift to component-based design systems where it can sometimes feel as though different parts of an interface are built by entirely different teams (or in this case, models).

How GenPage works instead

Instead of separate workers each handling a piece of the page, GenPage uses one model that builds the whole homepage in a single pass, the same way an LLM builds a response one word at a time.

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