{"id":119852,"date":"2026-10-07T11:11:42","date_gmt":"2026-10-07T05:41:42","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/multi-agent-systems-patterns-architectures-explained-with-examples\/"},"modified":"2026-10-07T11:12:55","modified_gmt":"2026-10-07T05:42:55","slug":"multi-agent-systems-patterns-architectures-explained-with-examples","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/multi-agent-systems-patterns-architectures-explained-with-examples\/","title":{"rendered":"Multi-Agent Systems: Patterns, Architectures and Examples"},"content":{"rendered":"\n

Multi-agent systems use multiple AI agents<\/a> that work together to complete tasks that are too complex, specialized, or large for a single agent. <\/p>\n\n\n\n

Instead of giving one agent every responsibility, a multi-agent architecture divides the workflow across agents with defined roles, communication paths, and decision-making responsibilities.<\/p>\n\n\n\n

For example<\/strong>, one agent can coordinate the workflow, another can research information, and a third can validate the result. The agents can work sequentially, in parallel, or under the control of a supervisor depending on the architecture.<\/p>\n\n\n\n

The right multi-agent pattern depends on factors such as task complexity, agent specialization, coordination requirements, latency, and reliability<\/strong>. <\/p>\n\n\n\n

This guide explains the most common multi-agent patterns, their architectures, practical use cases, and the failure modes engineers and architects should consider when designing agent teams.<\/p>\n\n\n\n

What Is a Multi-Agent System?<\/strong><\/h2>\n\n\n\n

A multi-agent system is an AI architecture in which multiple agents collaborate to achieve a shared goal<\/strong>. Each agent typically has a defined role, access to specific tools or knowledge, and responsibility for one part of the overall workflow.<\/p>\n\n\n\n

A simple multi-agent workflow can look like this:<\/p>\n\n\n

\"\"<\/figure>\n\n\n\n

The agents may share information through a central state, messages, events, or another coordination mechanism.<\/p>\n\n\n\n

The key advantage is specialization<\/strong>. Instead of building one large agent that must reason about every task, developers can create smaller agents optimized for specific responsibilities. <\/p>\n\n\n\n

Depending on how these agents coordinate, common multi-agent system patterns include the supervisor pattern, hierarchical pattern, peer-to-peer pattern, and pipeline pattern<\/strong>.<\/p>\n\n\n\n

However, adding more agents does not automatically improve performance. More agents can also introduce communication overhead, coordination failures, duplicated work, increased latency, and additional points of failure<\/strong>. <\/p>\n\n\n\n

Model selection also plays an important role in optimizing multi-agent architectures. For a deeper look at how smaller models can reduce latency and costs while handling specialized agent tasks, explore smaller models in multi-agent systems<\/strong><\/a>.<\/p>\n\n\n\n

Therefore, the architecture should be selected based on the complexity of the task and the level of coordination the application requires.<\/p>\n\n\n\n

1. Supervisor Pattern<\/strong><\/h2>\n\n\n\n

The supervisor pattern<\/strong> uses a central AI agent to coordinate multiple specialized agents. The supervisor receives the user's request, breaks it into subtasks, decides which agent should handle each task, and combines the resulting outputs.<\/p>\n\n\n\n

Architecture<\/strong><\/h3>\n\n\n
\"\"<\/figure>\n\n\n\n

The supervisor acts as the orchestrator<\/strong>, while the other agents focus on specific capabilities. For example<\/strong>, a research agent can gather information, a coding agent can generate implementation logic, and a review agent can check the result.<\/p>\n\n\n\n

Example<\/strong><\/h3>\n\n\n\n

Consider a software development assistant that receives:<\/p>\n\n\n\n

\"Analyze this API specification and suggest implementation changes.\"<\/p>\n\n\n\n

The supervisor could route the workflow as follows:<\/p>\n\n\n\n

    \n
  1. Send the API specification to the Research Agent<\/strong> to identify relevant requirements.<\/li>\n\n\n\n
  2. Send the requirements to the Coding Agent<\/strong> to propose implementation changes.<\/li>\n\n\n\n
  3. Send the proposed solution to the Review Agent<\/strong> to identify errors or missing requirements.<\/li>\n\n\n\n
  4. Combine the outputs and generate the final recommendation.<\/li>\n<\/ol>\n\n\n\n

    When to Use It<\/strong><\/h3>\n\n\n\n

    Use the supervisor pattern when an application has multiple specialized agents and requires dynamic task routing<\/strong>. It works particularly well when the next step depends on the result of the previous step or when the system needs a central component to control execution.<\/p>\n\n\n\n

    Failure Mode<\/strong><\/h3>\n\n\n\n

    The supervisor can become a single point of failure<\/strong>. If it incorrectly interprets the user's request or selects the wrong agent, downstream agents may perform irrelevant work. It can also become a bottleneck when many tasks need to pass through the same supervisor.<\/p>\n\n\n\n

    To reduce these risks, use clear agent descriptions, structured task inputs, routing rules, and validation of important decisions.<\/p>\n\n\n\n

    2. Hierarchical Pattern<\/strong><\/h2>\n\n\n\n

    A hierarchical multi-agent system<\/strong> organizes agents into multiple levels. A top-level agent manages the overall objective, while lower-level supervisors coordinate specialized agents responsible for individual domains or subtasks.<\/p>\n\n\n\n

    Architecture<\/strong><\/h3>\n\n\n
    \"\"<\/figure>\n\n\n\n

    Instead of one supervisor managing every agent directly, responsibility is distributed across multiple levels.<\/p>\n\n\n\n

    Example<\/strong><\/h3>\n\n\n\n

    Imagine an enterprise research system that needs to prepare a technical market report.<\/p>\n\n\n\n

    The Root Agent<\/strong> defines the overall research objective and delegates major areas to two supervisors:<\/p>\n\n\n\n