Building Multi-Agent Systems with MCP

Multi-agent systems built on MCP use multiple AI agents, each connected to specialized MCP server stacks, coordinating through shared servers or a supervisory layer to solve problems that no single agent could handle alone. MCP is uniquely suited for multi-agent architectures because its composability property allows any MCP client to also act as an MCP server, enabling hierarchical agent topologies where a supervisor agent delegates to specialized sub-agents.

Single-agent setups work well for focused tasks: answer a question, write a function, query a database. But complex workflows -- refactoring an entire codebase, managing an incident response, orchestrating a content pipeline -- require multiple agents with different capabilities working in coordination. MCP provides the standardized tool interface that makes this coordination possible.

For the foundational concepts of MCP and AI agents, see the parent guide: MCP for AI Agents: Building Autonomous Workflows.

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Why Multi-Agent Architectures

A single AI agent connected to every available MCP server quickly runs into practical limits:

Multi-agent systems solve these problems by dividing responsibilities. Each agent has a focused set of MCP servers, a clear role, and a defined interface with other agents.

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Architecture Patterns

Three primary patterns define how multiple MCP-connected agents can be organized.

Hub-and-Spoke (Supervisor Pattern)

A central supervisor agent coordinates multiple specialist agents. The supervisor decides which specialist to invoke, passes context, and synthesizes results.

                    Supervisor Agent
                   /       |        \
                  /        |         \
        Code Agent    Test Agent    Deploy Agent
           |              |              |
     +-----------+   +--------+    +-----------+
     | Filesystem|   | Shell  |    | Docker    |
     | GitHub    |   | Jest   |    | AWS       |
     | Search    |   | Python |    | Terraform |
     +-----------+   +--------+    +-----------+

How it works with MCP:

• The supervisor agent is an MCP client connected to sub-agents that expose themselves as MCP servers
• Each sub-agent is both an MCP client (connecting to tool servers) and an MCP server (exposing its capabilities to the supervisor)
• The supervisor sees tools like "write_code", "run_tests", "deploy_service" -- each backed by a specialized agent with its own tool stack

Best for: Sequential workflows where tasks have clear dependencies (code, then test, then deploy).

Mesh (Peer-to-Peer Pattern)

Agents communicate directly with each other, each exposing its capabilities as MCP server endpoints. There is no central coordinator.

     Code Agent <-----> Test Agent
          \                /
           \              /
            v            v
          Review Agent <-----> Deploy Agent

How it works with MCP:

• Each agent runs both an MCP client and an MCP server
• Agents discover each other's tools through their MCP server interfaces
• The code agent can directly call the test agent's "run_tests" tool, and the test agent can call the code agent's "fix_bug" tool

Best for: Collaborative workflows where agents need bidirectional communication (code review cycles, iterative debugging).

Hierarchical (Tree Pattern)

A layered approach where a top-level agent delegates to mid-level coordinators, which in turn delegate to specialized workers.

                  Executive Agent
                 /               \
         Dev Lead Agent      Ops Lead Agent
         /          \           /         \
   Frontend    Backend     Monitoring   Security
    Agent       Agent       Agent        Agent
      |           |           |            |
   [MCP       [MCP        [MCP         [MCP
   Servers]   Servers]    Servers]     Servers]

How it works with MCP:

• Three layers of MCP client-server relationships
• The executive agent sees high-level tools: "develop_feature", "ensure_reliability"
• Mid-level agents break these into specific tasks for worker agents
• Worker agents execute concrete operations using their MCP tool servers

Best for: Large-scale operations with clear organizational structure (enterprise workflows, complex project management).

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Shared MCP Servers Across Agents

Some MCP servers should be accessible to multiple agents in a multi-agent system. Database servers, logging servers, and communication servers are common examples.

Connection Patterns for Shared Servers

Dedicated connections (each agent connects independently):

Code Agent -----> PostgreSQL MCP Server
Test Agent -----> PostgreSQL MCP Server
Deploy Agent ---> PostgreSQL MCP Server

Each agent maintains its own connection to the shared server. This is the simplest approach and works well for stateless tool servers where each call is independent.

Proxy pattern (shared access through a gateway):

Code Agent --\
Test Agent ---}--> MCP Gateway --> PostgreSQL MCP Server
Deploy Agent-/

A gateway MCP server sits in front of the shared server, providing:

• Connection pooling (fewer connections to the backend)
• Access control (different agents get different permissions)
• Rate limiting (prevent one agent from monopolizing the resource)
• Audit logging (centralized record of who accessed what)

Conflict Resolution

When multiple agents can write to the same resource, conflicts must be handled:

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Agent Specialization

The power of multi-agent MCP systems comes from specialization. Each agent is configured with a focused set of MCP servers and a system prompt that defines its role.

Example Specializations

Research Agent:

MCP Servers:
  - Brave Search (web search)
  - Fetch (URL content retrieval)
  - Filesystem (read-only, for saving research)

System Prompt: You are a research specialist. Your job is to find
accurate, up-to-date information on topics assigned to you. Cite
your sources. Save summaries to the research directory.

Code Agent:

MCP Servers:
  - Filesystem (read-write, project directory only)
  - GitHub (create branches, PRs, read issues)
  - Shell (run build commands, linters)

System Prompt: You are a software engineer. Write clean, tested code.
Follow the project's coding standards. Create feature branches and
submit PRs for review.

QA Agent:

MCP Servers:
  - Filesystem (read-only, project directory)
  - Shell (run test suites, coverage tools)
  - Browser (Playwright for E2E testing)

System Prompt: You are a QA specialist. Run the test suite, analyze
failures, check coverage, and perform E2E testing on deployed
features. Report bugs with reproduction steps.

DevOps Agent:

MCP Servers:
  - Docker (container management)
  - AWS/GCP (cloud infrastructure)
  - Shell (deployment scripts)
  - Monitoring (Datadog, Grafana queries)

System Prompt: You are a DevOps engineer. Handle deployments,
monitor infrastructure health, and respond to incidents. Follow
the deployment runbook. Never deploy without passing CI.

Choosing Server Stacks for Agents

Browse the MCP server directory to find servers suited to each agent role.

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Coordination Patterns

Multi-agent systems need coordination mechanisms to work effectively.

Task Handoff

The simplest coordination pattern: one agent completes its work and hands the result to the next agent.

Research Agent
  --> produces: research_summary.md
    --> Code Agent reads research_summary.md
      --> produces: feature branch
        --> QA Agent checks out feature branch
          --> produces: test_report.md
            --> Supervisor reviews test_report.md

Each agent operates independently. Coordination happens through shared artifacts (files, database records, messages) rather than direct communication.

Shared State Store

Agents read from and write to a common state store, using it to coordinate:

# Shared state accessible to all agents via a State MCP Server
state = {
    "current_phase": "development",
    "assigned_tasks": {
        "code_agent": ["implement-auth", "implement-api"],
        "test_agent": ["write-auth-tests"],
        "deploy_agent": []  # waiting for tests to pass
    },
    "completed_tasks": ["design-review"],
    "blockers": []
}

The State MCP Server exposes tools like get_state, update_task_status, and add_blocker that any agent can call.

Event-Driven Coordination

Agents publish events when they complete actions, and other agents subscribe to relevant events:

Code Agent publishes: "feature/auth-module committed"
  --> Test Agent (subscribed to commit events) triggers test run
  --> Review Agent (subscribed to commit events) starts code review

Test Agent publishes: "all tests passing"
  --> Deploy Agent (subscribed to test results) starts staging deploy

Deploy Agent publishes: "staging deployment complete"
  --> QA Agent (subscribed to deployments) starts E2E tests

This decoupled approach scales well and allows agents to be added or removed without changing other agents.

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Real-World Multi-Agent Examples

Autonomous Development Team

A multi-agent system that handles feature development end-to-end:

The supervisor agent maintains a task board and assigns work based on the current project state.

Incident Response System

A multi-agent system for handling production incidents:

Agents work in parallel: the diagnosis agent investigates while the communication agent keeps stakeholders informed, and the remediation agent prepares rollback options.

Content Pipeline

A multi-agent system for content creation and publishing:

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Practical Considerations

Latency

Each layer in a multi-agent system adds latency. A supervisor calling a sub-agent, which calls an MCP server, which calls an external API, creates four network hops. Design for this:

• Keep critical paths shallow (two layers maximum)
• Run agents and servers on the same network when possible
• Use async patterns so agents can work in parallel
• Cache frequently accessed data at the agent level

Cost

Multi-agent systems multiply LLM API costs. Every agent invocation is an API call, and deep agent hierarchies can generate hundreds of calls for a single user request.

Observability

Multi-agent systems are hard to debug without proper observability. Implement:

• Trace IDs that follow a request through every agent and server
• Structured logging with agent name, task ID, and parent agent in every log entry
• Centralized log aggregation so you can reconstruct the full workflow
• Metrics on agent latency, success rates, and tool usage patterns

See the MCP Monitoring and Observability guide for implementation details.

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What to Read Next

• MCP for AI Agents: Building Autonomous Workflows -- the parent guide on MCP and AI agents
• MCP Agent Orchestration Patterns -- design patterns for coordinating tool execution within agents
• MCP Architecture Explained -- understanding the composability that enables multi-agent systems
• Composability in MCP -- how MCP clients can be servers, enabling hierarchical architectures
• Browse MCP Servers -- find servers to build your agent stacks