Artificial Intelligence (AI) is evolving beyond single, monolithic models. Today’s most capable systems are made up of multiple AI agents. This new paradigm of multi-agent systems (MAS) represents the next step in AI evolution, where collaboration and coordination between agents matter as much as individual intelligence.
In this article, we’ll break down what multi-agent systems are, how they work through real-world analogies, and explore the different architectures that make them scalable and effective in practice.
LLM Agents
With the rapid development of Large Language Models (LLMs), many systems are now built around an LLM core (ChatGPT, Claude, LLaMA, etc.) and extended with prompts, functions, or pipelines to perform specific tasks. These systems are known as LLM Agents.
There are several types of LLM Agents, depending on how they are implemented:
Tool-calling agents: These agents use the LLM to interact with external functions or tools, like searching a database or accessing an API.
Graph agents: Instead of tackling a prompt all at once, this agent breaks a complex task into smaller steps. Each step might call a specific prompt or function, with the output of one step feeding into the next. In this method, agents are built as a directed graph or state machine, each node representing reasoning or tool-calling steps. Popular frameworks to develop this type of agent are LangGraph, CrewAI, LLamaIndex, …
Planning Agents: These agents create a plan of action before executing steps. They break complex goals into smaller tasks by using prompting methods. A simple setup might include two stages: First is planning, where the agent analyzes the request and outlines step-by-step actions. Second is execution, the agent executes the plan, combining the original request with the generated steps to deliver the final output. Modern frameworks such as AutoGPT, BabyAGI, and CAMEL have expanded and refined this approach.
Reasoning Agents: Currently, one of the most widely adopted approaches, inspired by the 2023 paper “ReAct: Synergizing Reasoning and Acting in Language Models.” These agents simulate human-like logical reasoning. Upon receiving a request, the agent thinks, decides, and reasons through what it needs to do, then executes an action, observes the result, and updates its state. This loop of think → act → observe → update continues until the goal is reached or a stopping condition is met. Unlike Planning Agents, who create a fixed plan and execute it in one go, Reasoning Agents work in a continuous feedback loop.
Multi-Agent Systems
A multi-agent system is exactly what it sounds like: a system that combines multiple, individual agents, allowing them to work together to handle tasks far more complex than any single agent could manage alone. These agents can collaborate in different ways, depending on how the system is designed.
Let’s check out the three most common multi-agent architectures to see how each one shapes coordination, scalability, and performance.
1. Network
Imagine you're starting a company with three friends: a Back-end developer, a Front-end developer, and an admin. Everyone knows each other well and collaborates closely.
In the early days of the company, your main goal is to find as many customers as possible, so external connections and relationships become crucial.
When a customer reaches out to any member of your team to request a product or service, that person will notify the others and check whether they are capable of handling the request. If they can, they complete the task and deliver the result to the customer. If not, or if they can only handle part of it, they will reach out to the rest of the team to find someone who can continue or complete the process.
Simple Scenario: A customer who knows your Back-end developer asks for a simple Application Programming Interface (API) for their business. The Back-end developer can handle this alone, complete the work, and deliver the final product directly to the customer.
Complex Scenario: The same customer returns, but this time they want a full application - a User Interface (UI), more APIs, and a server to deploy everything on. The Back-end developer can't do this alone. He breaks the project down and asks each team member to contribute their part. Once all the pieces are finished, he assembles the final product and delivers it to the customer.
This is the core concept of a Network Multi-Agent Architecture. Each agent in the system handles a specific task, and they are all interconnected. Every agent is aware of the others and their unique capabilities. You set one agent as the entry point; when a request comes in, this agent analyzes whether it can handle it alone or if it needs to pass the task (or parts of it) to other agents in the network.
Technical Example: You have three agents - a Search Agent (to pull data from a database), a Calculate Agent (for numerical tasks), and a Chart Agent (to create visualizations).
When you ask the system to "calculate the company's revenue last year," the request will be handled as below:
This architecture is great for small systems with fewer than ten agents; you can connect them directly and don't have to think about their relationship or hierarchy. However, it has two main disadvantages:
Scalability: As your system grows, adding a new agent becomes a headache. You have to establish a connection from the new agent to all existing agents and then update all existing agents to recognize the new one.
Cost: To work effectively, each agent needs context about all the other agents in its prompt. As the system gets more complex, these prompts grow larger, consuming more tokens for every request. This can quickly become expensive and even hit API limits, causing complex requests to fail.
2. Supervisor
Your company is growing. With more customers and tasks pouring in daily, you want your teammates to focus on working in their specific domain instead of finding more clients for your business. You’ve also hired some specialists from different domains to expand the company’s tech stack. The structure of your company becomes a little different.
Everybody is now under your management, and you’ll act as the central supervisor. When a client comes with a request, they meet with you. You analyze their requirements, delegate the work to the right person, and manage the process. Once the task is complete, the team member reports back to you for final verification before you deliver the result to the client.
Simple Scenario: A client comes to your company to request a custom AI for analytical tasks. You assign this directly to your AI Engineer. Once the engineer has built and tested the AI, they deliver it to you, and you present the final product to the client.
Complex Scenario: A client wants a full-featured application with an integrated AI, a User Interface, a Back-end, and CI/CD (Continuous Integration/Continuous Deployment) pipelines. Now, you must act as a project manager. You break the request into smaller tasks and create a workflow:
- Step 1: You assign the research and creation of the analytical AI to the AI Engineer.
- Step 2: Once the AI model is ready, you pass it to your Back-end engineer to integrate it into a robust server-side application.
- Step 3: When the back-end is complete, you ask your Front-end engineer to build a user interface that communicates with it.
- Step 4: Finally, you bring everything to your DevOps engineer to create the CI/CD pipelines and deploy the application on a server.
- Step 5: You wrap up the finished project and deliver it to the client.
In this model, a central coordinator agent (you) connects to all other agents in a 1-to-1 relationship. This coordinator's job is to analyze requests, break them down, and delegate tasks to the appropriate specialist agent.
Technical Example: You still have the Search Agent, Calculate Agent, and Chart Agent. Instead of connecting them together, you create a coordinator to maintain a 1:1 connection between this coordinator and the others.
Let's revisit the request to "calculate the company's revenue last month." With a supervisor agent managing the process, the workflow is far more structured:
Unlike the network model, the specialist agents don't need to know about each other; they only communicate with the coordinator. This allows them to focus purely on their tasks.
However, this architecture introduces a new bottleneck: the coordinator. As you add more agents to the system, the coordinator's prompt grows larger with information about each new agent it manages. Eventually, it too can become unwieldy and hit token limits. That’s the reason why we have another architecture to handle this, and it’s called hierarchical.
3. Hierarchical
Your company is now a major player in the industry, with a large staff and many customers. You can't possibly manage everyone individually, much like Napoleon couldn't remember the name of every soldier in his army. So, you organize the company into departments.
Each department groups people with similar skills. You appoint a manager for each department. Now, instead of assigning tasks to individuals, you assign a high-level goal to a department manager. That manager is then responsible for delegating the work to the right people on their team and reporting the results back to you. This structure can have multiple layers, with departments containing smaller, specialized groups.
This is the Hierarchical Multi-Agent Architecture. This model solves the supervisor's bottleneck. The top-level coordinator only communicates with a few high-level agents (or "sub-coordinators"). These sub-coordinators, in turn, manage their own teams of specialized agents.
The workflow is similar to the supervisor model but with more layers. A request comes to the main coordinator, which analyzes it and routes it to the most relevant sub-coordinator. The sub-coordinator then breaks the task down further and assigns it to the specific agent best suited for the job. The result is passed back up the chain, from the specialist agent to the sub-coordinator, and finally to the top-level coordinator who delivers the final output.
Agent as a Tool
In modern development, the concept of “agents as tools” often overlaps with hierarchical or supervisor systems.
Instead of coding separate functions for each tool, you can treat an agent as a domain-specific tool that handles a group of related tasks. Then, you can treat that entire multi-agent system as a single, powerful "tool" that a higher-level agent can call upon. This is a highly effective way to encapsulate complex capabilities and build incredibly sophisticated systems.
Conclusion
A multi-agent system provides a structured way to manage multiple LLM agents within a single ecosystem:
Network architecture works best for small systems with fewer agents.
Supervisor architecture suits medium-sized systems.
Hierarchical architecture is ideal for large-scale setups.
Each approach has trade-offs in scalability, complexity, and efficiency. Based on your needs, you can choose the most suitable architecture and framework.
To get started, it’s worth exploring LangGraph and LlamaIndex as they are among the most popular frameworks for developing LLM and multi-agent systems. LangGraph also provides tutorials for building multi-agent setups.
If you’re wondering how to connect agents built with different frameworks or languages, explore Agent-to-Agent communication and MCP (Model Context Protocol). Combining those concepts with the architectures above will help you design an adaptive, scalable multi-agent system.
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