Multi Agent Architecture explained
🧠 Knowledge Series #72: The 5 common types of AI agent architectures explored with real world examples
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If you’re feeling a little fatigued by the AI hype, then take a deep breath and consider this: former Google CEO Eric Schmidt said in a recent TED talk that he believes that the AI revolution is actually “underhyped”.
And part of the reason he believes this is because of the potential of AI Agents.
In the future, Schmidt argues, AI agents will handle tasks by working together in various ways to handle complex business processes and solve critical problems. And it’s this process of multiple AI agents working together that we’re going to focus on in this Knowledge Series.
We’ve previously looked at how to build an AI agent but in this Knowledge Series we’re going to take things a step further by exploring the emerging area of multi agent AI architectures and the technologies that underpin them. We’ll look at the most common types of multi agent architectural patterns along with some real world examples from top companies to bring everything to life.
If you’re curious about how AI agents work, and in particular how they can work together to achieve common goals, then this Knowledge Series should help.
Coming up:
What exactly is multi agent AI architecture?
5 types of common multi agent architectures explored
Real world examples of multi agent architecture in practice from top companies
The future of work and how to implement and manage AI agents
Essential terminology and technologies worth knowing about
What exactly is multi agent AI architecture?
Essentially, multi-AI agent architecture refers to a system where multiple AI agents, usually with their own set of specialized skills or roles, collaborate together to solve a complex problem or achieve a shared goal.
This type of architecture uses the unique strengths of each individual agent to help it achieve its goals. In an AI agent context, these strengths can include things like having a distinct piece of specialist knowledge, a decision making style or task-specific skills such as image recognition.
The benefits of multi agent systems
One of the reasons that multi agent systems have been adopted recently is because a single AI agent can become overwhelmed with too many tools, too much context and too broad a knowledge base that makes it difficult to manage.
The main benefits of using multi agent systems are:
Modularity - separate agents make it easier to develop, test, and maintain agentic systems.
Specialization - you can create expert agents focused on specific domains, which helps with the overall system performance.
Control - you can explicitly control how agents communicate.
But, there are a few downsides to be aware of, too. We’ll come to those later. First, let’s take a look at the most common architectural styles.
5 types of common AI agent architectures explored
Here’s a snapshot of some of the most common AI agent architectural styles that have emerged over the past few months:
This is still a rapidly developing area of technology and so may change again but given the current abilities of AI agents, if product teams were to use AI agents or build features that use them, they’d typically adopt one of these approaches.
A closer look at each of these multi AI agent architectures
Now let’s take a closer look at each of these multi AI Agent architectural styles to fully understand what they are, how they can be used in the real world along with some potential downsides to be aware of.