Multi-Agent Systems: Coordinating Multiple AI Agents as a Team

What This Article Covers

• Learn what a multi-agent system is and how it differs from a single AI agent.
• Understand the main architectures: hierarchical, peer-to-peer, ring, and star.
• Discover typical agent roles such as orchestrator, researcher, writer, reviewer, coder, and tester.
• See a concrete example of how a content production team of agents works through tasks step by step.
• Find out which frameworks are suitable and what pitfalls to avoid.

Introduction: Multi-Agent Systems Explained

A single AI agent is impressive on its own. It can answer questions, invoke tools, and remember previous steps. But the moment a task requires multiple steps, diverse skills, and clear division of labor, a lone agent hits its limits. Its context window fills up, focus drifts, and results become unreliable.

That’s where multi-agent systems come in. Instead of one agent doing everything, you deploy a team of specialized agents. Each has a clearly defined role, its own tools, and a focused context. Agents exchange information, coordinate with each other, and deliver a result that a single agent couldn’t achieve alone.

This article is written for newcomers. You don’t need prior knowledge of multi-agent systems, just a basic understanding of what an AI agent is. If you’re missing that foundation, start with What is an AI Agent?. If you’re already familiar with agent systems, this article is your natural next step.

Why Do You Need Multi-Agent Systems?

Imagine you want to build a software feature. A single agent writes the code, reviews it himself, and then tests it. He starts writing, switches to reviewing, realizes he introduced a bug, goes back to writing, loses sight of the requirements, and ends up in chaos with half-finished tasks. The prompt grows longer, context overflows, and the agent makes mistakes because he’s playing too many roles at once.

Now instead, you assemble a team of specialized agents. A coder agent writes the code. A reviewer agent checks it for bugs and style. A tester agent writes and runs tests. An orchestrator agent coordinates the workflow and collects results. Each agent has a clear job, a focused prompt, and only the tools it needs. The overall system stays manageable, even as the task grows.

The difference is tangible: instead of one overloaded agent trying to do everything, you have a team that tackles the problem systematically. Each agent focuses on its strength, and a dedicated role handles coordination. The result is more robust, easier to debug, and simpler to extend.

Multi-Agent Systems in Brief

A multi-agent system is like a project team in a company. Imagine a team building a new product. There’s a designer who creates the concept, a developer who plans implementation, a tester who checks quality, and a reviewer who approves the result. Each has their task, their tools, and their perspective on the project. A coordinator distributes work and brings results together.

Translated to AI agents: each agent is its own language model with its own prompt, tools, and memory. Agents communicate through messages or a shared storage called Shared Memory. A structure called topology defines who talks to whom and in what order. An orchestrator ensures the team works toward the same goal.

In short: a multi-agent system is an architecture where multiple AI agents with distributed roles solve a complex task together, one that a single agent couldn’t handle.

Who Are Multi-Agent Systems For?

Multi-agent systems are for developers who want to use AI agents productively and hit the limits of a single agent. If you’ve already built simple agents with tool-calling and found that one agent isn’t enough, you’re in the right place.

Teams automating AI pipelines also find the toolkit here for scalable architectures. Whether content creation, software development, data processing, or customer support, as soon as a task breaks down into substeps and requires different skills, a multi-agent system pays off.

Prerequisite: you should know the fundamentals. If you’re uncertain, start with the AI Agent Basics and the article on agent systems.

Key Terms in Multi-Agent Systems

Term	Meaning
Multi-Agent	A system with multiple agents that coordinate and work together
Role	The task an agent assumes in the system, for example researcher or reviewer
Orchestrator	An agent that controls overall execution and coordinates other agents
Delegation	Passing a subtask from one agent to another
Shared Memory	A common storage that multiple agents access and use to exchange results
Message Passing	A communication pattern where agents send messages directly to each other
Topology	The structure that defines how agents connect to each other
Hierarchy	A topology with superior and subordinate agents
Peer-to-Peer	A topology where agents communicate directly with each other without central control
Broadcast	A message sent to all agents simultaneously

Architecture of Multi-Agent Systems

The architecture of a multi-agent system is determined by its topology. Topology defines who talks to whom and how information flows. Here are the main patterns:

Hierarchical Topology

An orchestrator sits at the top and controls multiple subordinate agents. It divides the task, assigns it to workers, and collects results. This pattern is clear and works well for complex tasks with distinct substeps.

            [Orchestrator]
             /     |     \
        [Worker] [Worker] [Worker]
             \     |     /
            [Result]

Example: An orchestrator assigns research tasks to three workers, each evaluating a source, then synthesizes the findings.

Peer-to-Peer Topology

All agents communicate directly with each other without central control. Anyone can talk to anyone. This pattern is flexible but hard to control and debug because there’s no clear hierarchy.

   [Agent A] <-> [Agent B]
      ^             ^
      v             v
   [Agent C] <-> [Agent D]

Example: Four agents discuss a question in a round and work out an answer together.

Ring Topology

In a ring topology, agents form a circular chain. Each agent passes its output to the next one until the loop closes. It’s straightforward to implement, but rigid. If a single agent fails, the entire ring breaks.

   [Agent A] -> [Agent B]
       ^            |
       |            v
   [Agent D] <- [Agent C]

Example: Agent A researches, Agent B summarizes, Agent C writes the article, Agent D reviews it and passes the result back to Agent A.

Star Topology

A central agent, typically the orchestrator, sits at the hub. All other agents communicate only with it, never directly with each other. This arrangement is easy to control because all information flows through one point.

            [Orchestrator]
           /   |   |   \
      [A]  [B]  [C]  [D]

Example: An orchestrator collects results from four workers and routes them forward. The workers don’t exchange information directly with one another.

Your choice of topology depends on the problem. For clear, structured workflows, a hierarchy works well. For tight collaboration with frequent coordination, peer-to-peer makes sense. For simple sequential tasks, a ring suffices. For centralized control, the star topology is the right fit.

Roles in a Multi-Agent System

Every agent in a multi-agent system takes on a role. The role determines what task the agent performs, which tools it can use, and how its prompt is structured. Here are the most common ones:

Orchestrator

The orchestrator is the coordinator. It receives the task, breaks it into subtasks, delegates them to other agents, and combines the results. It doesn’t do the actual work, but ensures the team moves in the right direction. Example: An orchestrator that takes a content request and delegates it to a researcher, writer, and reviewer.

Researcher

The researcher finds information. It uses search tools, queries databases or APIs, and collects relevant sources. Its prompt focuses entirely on locating and evaluating information. Example: A researcher that finds current studies on local AI for an article.

Writer

The writer produces text. It takes the collected information and turns it into a well-structured document. It doesn’t have search tools, only writing and formatting tools. Example: A writer that drafts a blog article from the researcher’s notes.

Reviewer

The reviewer checks the work of other agents. It reads the text, looks for errors, gaps, and contradictions, and provides feedback. If it finds problems, it sends the work back with suggestions for improvement. Example: A reviewer that checks an article for factual accuracy and readability.

Coder

The coder writes code. It takes a requirement, implements it, and delivers the result. Its prompt is optimized for programming, using tools like file access and compilers. Example: A coder that implements a Python function for data cleaning.

Tester

The tester writes and runs tests. It verifies that code from the coder agent produces expected results. When it finds bugs, it reports them to the orchestrator or directly to the coder. Example: A tester that writes and runs unit tests for the new function.

Roles aren’t fixed. You can combine them, define new ones, or modify existing ones. What matters is that each role is clearly distinct and no two agents perform the same task.

Example: A Content Production Team

Here’s a concrete example of a multi-agent system for creating content. The task: Write an article about the benefits of local AI and publish it.

Step 1: Orchestrator receives the task

The orchestrator analyzes the request and breaks it into subtasks: research, writing, review, publication. It determines which agents are needed and in what order they work.

Step 2: Research agent finds information

The research agent uses tool-calling to query sources. It searches the web for articles, studies, and documentation on local AI. It gathers key points and stores them in shared memory. Its prompt focuses exclusively on finding and evaluating relevant sources.

Step 3: Writer agent drafts the article

The writer agent reads the collected information from shared memory and composes a first draft. It has no search tools, only writing and formatting tools. Its prompt sets the tone, structure, and target audience.

Step 4: Editor agent reviews the draft

The editor agent reads the draft and checks it for errors, gaps, contradictions, and readability. It provides concrete feedback: this paragraph is too long, this source is missing, this term needs explanation. It sends the draft back to the writer with notes.

Step 5: Writer agent revises the draft

The writer agent incorporates the feedback and revises the draft. It shortens the long paragraph, adds the missing source, and explains the term. The revised draft goes back to the editor.

Step 6: Publisher agent publishes the article

Once the editor approves the draft, the orchestrator passes the article to the publisher agent. It uses a tool to load the article into the CMS, set metadata, and publish it. The orchestrator confirms success.

[Task]
   |
   v
[Orchestrator]
   |
   v
[Research Agent] -> [Shared Memory]
                        |
                        v
                 [Writer Agent] -> [Draft]
                        |
                        v
                 [Editor Agent] -- returns to Writer if needed
                        |
                        v
                 [Publisher Agent] -> [Published Article]
                        |
                        v
                 [Orchestrator] -> [Success]

This system is extensible. You can add a SEO agent that checks keywords or a fact-checker agent that verifies claims without changing the other agents. Each agent stays focused, and the overall system remains manageable.

Popular Frameworks for Multi-Agent Systems

Frameworks save you work by providing infrastructure for communication, delegation, and state management. Here are the most popular ones:

CrewAI

CrewAI is a framework that makes building multi-agent systems particularly straightforward. You define agents with roles, goals, and tools, combine them into a crew, and give the crew tasks. CrewAI handles the coordination. It’s well suited for newcomers and systems with clearly defined roles.

AutoGen

AutoGen comes from Microsoft and has a more flexible design. It supports conversations between agents, where multiple agents interact in a chat. AutoGen is better for complex scenarios where agents need to interact dynamically and coordinate with each other.

LangGraph

LangGraph extends LangChain by modeling multi-agent systems as graphs. You define nodes for agents and edges for transitions between them. LangGraph shines when you need complex workflows with conditions, loops, and branching logic.

Camel-AI

Camel-AI is a research-focused framework that emphasizes communication between agents. It was designed to study agent behavior in role-playing scenarios. Camel-AI works well for experimental setups and exploring new multi-agent architectures.

Your framework choice depends on your use case, programming language, and experience level. See the article on Frameworks for an overview. If you’re unsure, start with CrewAI because it has the gentlest learning curve.

Multi-Agent vs. Single Agent: When to Use Each

When do you need a multi-agent system, and when is a single agent enough? The answer depends on task complexity.

A single agent is right for well-defined tasks that need few tools and fit in a single prompt. Single agents are fast to build, easy to debug, and cost-effective. Examples: answering a question, summarizing text, running a simple web search.

A multi-agent system becomes necessary when a task breaks into several independent substeps, requires different roles, or benefits from parallel work. Multi-agent systems take more effort to build but scale better and are more robust. Examples: producing an article from research through publication, developing a software feature from implementation to testing, compiling a report from multiple sources.

Decision guide:

• Single agent when: one role, one prompt, few tools, straightforward task.
• Multi-agent system when: multiple roles, parallel work, complex substeps, scalability matters.

Rule of thumb: start with a single agent. Once your prompt becomes hard to manage, context grows too large, or your agent needs to play multiple roles at once, switch to a multi-agent system.

Common Pitfalls in Multi-Agent Systems

Multi-agent systems are powerful but come with real challenges. Here are the most frequent ones:

1. Coordination Overhead

Every agent needs to know what to do, when it’s their turn, and where to pass results. Without clear coordination, chaos ensues. An orchestrator helps but must be configured cleanly. The more agents you have, the higher the coordination burden.

2. Infinite Loops

A reviewer agent sends text back, a writer agent improves it, the reviewer finds something else, and round and round it goes. Without an exit condition, the system runs forever and burns through tokens. Always set a maximum iteration count.

3. Cost Explosion

Each agent call costs tokens. In a multi-agent system, costs multiply quickly because multiple agents run multiple times per task. Use small models for simple tasks like routing and save large models for complex reasoning.

4. Agent Conflicts

When two agents have overlapping tasks, conflicts emerge. One overwrites the other’s work, or results contradict each other. Define each role sharply and distinctly so no overlap occurs.

5. Communication Failures

When agents communicate only through messages, information gets lost or misinterpreted. Shared memory helps but must sync cleanly, or agents work with stale data. Ensure every agent reads the current state.

6. Hard Debugging

In a multi-agent system, it is not immediately obvious which agent caused a failure. Was it the researcher providing wrong sources? The writer misinterpreting them? The reviewer missing the error? Logging is mandatory. Every agent should log its steps, tool calls, and results.

7. Unclear Roles

If roles aren’t cleanly defined, no agent knows what they are responsible for. The writer starts researching, the researcher starts writing, and everyone ends up doing everything. Define roles, responsibilities, and handoffs clearly.

8. Neglected Security

Agents with access to APIs, file systems, and databases can cause damage if they make wrong decisions. Limit each agent’s tools to what is necessary and use permission checks. A publisher agent needs write access to the CMS, but a researcher does not.

Hardware, Costs, and Security in Multi-Agent Systems

Multi-agent systems demand more from hardware than single agents because multiple agents run in parallel or sequence. If you use local models, you need sufficient VRAM. An orchestrator with a small model like Llama 3.2 3B runs on modest hardware, but a writer with an 8B model needs considerably more. Local AI with tools like Ollama makes it possible to run different model sizes in parallel.

Costs with cloud models add up fast. A system with five agents, each called multiple times per task, can be significantly more expensive than a single agent. Mix small models for routing and simple tasks with large models only for complex reasoning. Per-agent token limits prevent runaway cost from looping agents.

Security matters more in multi-agent systems because multiple agents access different systems. Each agent should only get the tools it needs for its role. A research agent needs no database write access, and a reviewer should not have API keys for external services. Work with clear permissions, logging, and human checkpoints for critical actions.

Further Reading and Resources on Multi-Agent Systems

• What is an AI Agent? - The basics if you are just starting
• Agent Systems - Architecture and design of agent systems
• Tool-Calling - How agents invoke tools
• Agent Memory - How agents remember
• Frameworks - Tools for building multi-agent systems
• CrewAI - Framework for simple multi-agent systems
• AutoGen - Framework for conversational agents
• LangGraph - Framework for graph-based agent workflows
• AI Agent Basics - Overview of all foundational articles
• Ollama - Run local models easily

FAQ: Multi-Agent Systems - Common Questions

What is a multi-agent system?

A multi-agent system is an architecture of multiple AI agents with distributed roles working together to solve a complex task. Each agent has a clearly defined job, its own tools, and a focused context. Agents communicate through messages or a shared store.

How does a multi-agent system differ from a single agent?

A single agent handles all work alone. A multi-agent system distributes work across multiple agents with clear roles, making complex tasks more organized, robust, and scalable.

When do I need a multi-agent system?

Once a task breaks into multiple independent substeps, requires different roles, or overloads a single agent’s context, a multi-agent system makes sense. Start with a single agent and switch when your prompt becomes unwieldy.

What is an orchestrator?

The orchestrator is an agent that controls overall execution. It takes the task, breaks it into substeps, distributes work to other agents, and reassembles results.

Which topology is best?

It depends on your task. Hierarchy works for clear, structured workflows. Peer-to-peer suits close collaboration with lots of coordination. Ring works for simple, sequential flows. Star topology is right for centralized control.

What is shared memory?

Shared memory is a common store that multiple agents access. It enables result exchange without direct communication between agents. Every agent can read and write, making exchange straightforward.

What is message passing?

Message passing is a communication pattern where agents send messages directly to each other. Unlike shared memory, there is no common store, just information passed as messages from agent to agent.

How do I avoid infinite loops in multi-agent systems?

Define a maximum iteration count for each cycle. A reviewer agent should give up after a fixed number of attempts and return the best result so far. Without an exit condition, the system runs forever.

Which framework suits multi-agent systems?

Several frameworks exist. CrewAI is beginner-friendly and good for systems with clear roles. AutoGen suits conversational agents. LangGraph is powerful for complex workflows with conditions and loops. Your choice depends on your task and experience.

Can I run multi-agent systems locally?

Yes. With tools like Ollama, you can run local models and build multi-agent systems on your own hardware. Ensure sufficient VRAM, especially if multiple agents run in parallel. A mix of small and large models keeps hardware demands reasonable.

How much do multi-agent systems cost?

Costs depend on agent count, model size, and call frequency. Cloud models get expensive quickly because calls multiply. A mix of small and large models plus per-agent token limits keep costs in check.

How do I debug a multi-agent system?

Logging is key. Every agent should log its steps, tool calls, and results. This way you can trace which agent did what and where things went wrong. Without logging, debugging multiple agents is nearly impossible.

Sources and further reading

• What is an AI agent? - Foundational article on BotServ.de
• Agent systems - Architecture and design on BotServ.de
• Tool calling - Basics of tool invocation on BotServ.de
• Agent memory - Storage and recall on BotServ.de
• Frameworks - Overview on BotServ.de
• CrewAI - Framework introduction on BotServ.de
• AutoGen - Framework introduction on BotServ.de
• LangGraph - Framework introduction on BotServ.de
• Ollama - Local model management on BotServ.de