How Multi-Agent Systems Work: Key Features and Benefits

AI is evolving at a rapid pace. And the way it’s built and orchestrated is also changing. Specifically, AI agents are revolutionizing how various industries work (including game development). 

Yes, a single autonomous agent can automate all kinds of workflows and processes. However, an entire team of agents can be even more impactful. We are referring to multi-agent systems. 

What exactly are these systems? How do they work? How can you build them? And who is leveraging MASs to great effect? Let’s go.

Single AI Agents vs. Multi-Agent Systems (MAS)

A single AI agent is basically a Learning Language Model (LLM) that uses tools provided by a system’s creators.

These systems are great for simple workflows. However, they can be limiting in what they can achieve. For example:

• They can perform poorly if inundated with too much context.
• Performing a wide range of tasks at a high level can be challenging.
• They can struggle to handle multiple tools, which can confuse decision-making.

As the name implies, a multi-agent system is a system with many agents that interact with each other. These agents work in collaboration, ensuring your system is not deterministic. 

How Do Multi-Agent Systems Work?

In multi-agent systems, just one of its agents’ outputs will impact the continuation of the entire running process. For example, if one agent’s output is “A,” we’ll go down a certain path in the system. But if it will output “B,” we’ll go down a different one. 

That means you can have multiple agents, but not all of them will necessarily activate each time you run the system. It all depends on the intermediate results you get in the process.

The agents need to be aware of who else exists in the environment and what tools are available to them. This allows them to decide the right path to take.

Specialized Agents in One System

In a multi-agent system, different types of agents do different things. And it’s possible to delegate tasks among specialized agents. 

For example, let’s say you’re developing an AI analytics tool that provides insights. One of your agents is tasked with generating these insights. To do its job, it needs results from another agent that can retrieve the reference group, which will be compared to the investigative group. So each pair of agents has a completely unique interaction.

Hierarchical Systems

Many multi-agent systems are hierarchical in structure. For example, many have one dominant agent, often called the “master agent.” 

While each agent thinks for itself, the master agent is like the big brain behind the entire system. It receives a trigger directly from the user, who demands an output. The master agent takes this input and operates as an “AI puppet master” – delegating specific tasks to the other agents.

So below the master agent, you have all the other agents performing specific tasks. And below these agents, are the tools they use.

Tools of a Multi-Agent System

Let’s say you want one of your agents to visualize some data. You can create a Python tool (like Matplotlib) specifically for data visualization. Once this is ready, you can give an agent the following prompt: 

“When I ask to visualize some data, you can use this tool.”

At this point, the agent:

• Received the information
• Knows it has the visualization tool at its disposal
• Knows to use it when provided with the relevant input (e.g. “provide me with a graph”).

There are numerous advantages to these systems. But they also present plenty of challenges. 

Pros and Cons of Multi-Agent Systems

Here are some of the upsides and pitfalls that multi-agent systems present:

Pros	Cons
Can be extremely flexible, adjusting to various environments by adding, removing, and adjusting agents.	Can be complex to build a system in which all agents effectively communicate with each other.
Each agent can specialize in specific areas, whereas single agents have to perform tasks in multiple areas.	Agents’ goals can clash, which can make processes stuck or inefficient.
Can handle large data sets, allowing them to solve more complex problems, making them ideal for scalability.	Managing multiple agent systems can require excessive computational resources.

Other potential challenges of MASs include the potential for malfunctions, complex integrations, and unpredictable behavior.

AI Multi-Agent Frameworks

AI frameworks best for building multi-agent systems include:

• PydanticAI: Great for agent delegation, hand-offs, and single-agent workflows.
• CrewAI: Assigns multiple agents with specialized roles and delegates tasks accordingly.
• MetaGPT: Constructs multi-agent collaboration based on human workflow simulations.
• Relevance AI: First no-code framework for building autonomous AI teams.
• LangGraph: Ideal for dynamic workflows, multi-step decision-making, and problem-solving.
• Microsoft AutoGen: Leading open-source frameworks for building LLM apps with multiple agents.
• OpenAI’s Swarm: Research tool that enables developers to build modular, specialized agents.

One platform leveraging a multi-agent system in the game development world is Keewano. 

Leverage Multiple Agents to Up Your Game Analytics

Keewano’s next-gen platform utilizes a multi-agent system to autonomously test various hypotheses on large user populations. It then feeds back distilled, actionable insights, providing game developers with an instant, accurate understanding of their game’s most pressing issues.

This system is enabled by a high-performance database, which can process 256 million events per second, 600 times faster than any existing solution.

This database supports multiple AI agents at scale. By leveraging advanced time-series statistical models, Keewano’s multi-agent platform automates root cause discovery for faster decision-making.