Multi-Agent Systems

Overview

A multi-agent system (MAS) distributes workflow execution across multiple specialized AI agents that coordinate to solve problems no single agent could handle efficiently on its own. Rather than building one monolithic agent that does everything, multi-agent architectures assign distinct roles to focused agents — a planning agent, a coding agent, a testing agent — and define how they communicate and hand off work.

Why Multiple Agents?

A single agent with access to dozens of tools and a massive system prompt becomes increasingly unreliable as complexity grows. Its context window fills up, its instructions conflict, and its decision-making degrades.

Multi-agent systems address this by applying the same principle that makes human teams effective: specialization. Each agent has a narrow role, a focused set of tools, and clear instructions for its domain.

Consider a software development workflow:

• Planning Agent — Analyzes requirements, breaks work into tasks, defines acceptance criteria
• Software Engineering Agent — Writes implementation code, performs refactoring, handles migrations
• QA Agent — Generates tests, runs test suites, validates behavior against acceptance criteria
• Review Agent — Checks code quality, security, adherence to conventions

Each agent excels at its role because it carries only the context and tools relevant to that role. The system as a whole handles complex workflows that would overwhelm any single agent.

Orchestration Patterns

Orchestration defines how agents coordinate — who decides what happens next, how information flows between agents, and how conflicts are resolved. The choice of orchestration pattern has a direct impact on system reliability, flexibility, and complexity.

Centralized Orchestration

A single conductor agent (sometimes called a master or supervisor) manages the entire workflow. It receives the initial task, decides which specialist agents to invoke, passes context between them, and assembles the final result.

How it works:

1. The conductor receives a task from the user
2. It breaks the task into subtasks
3. It dispatches each subtask to the appropriate specialist agent
4. It collects results and decides the next step
5. It assembles the final output

Strengths:

• Simple to reason about — one agent has the complete picture
• Easy to implement logging and observability
• Clear accountability for decisions

Weaknesses:

• The conductor is a single point of failure
• Can become a bottleneck as the number of specialist agents grows
• The conductor's context window must accommodate the full workflow state

Decentralized Orchestration

Agents communicate directly with each other in a peer-to-peer fashion. There is no central coordinator — agents pass work to the next agent in the chain based on their own assessment of what needs to happen next.

How it works:

1. An agent completes its task
2. It evaluates the result and decides which agent should handle the next step
3. It passes context directly to that agent
4. The process continues until the workflow is complete

Strengths:

• No single point of failure
• Scales naturally as agents are added
• Each agent only needs context for its immediate task

Weaknesses:

• Harder to monitor the overall workflow state
• Potential for loops or deadlocks if agents disagree
• Debugging requires tracing across multiple agent interactions

Hybrid Orchestration

Combines centralized and decentralized approaches. A conductor manages the high-level workflow while allowing specialist agents to coordinate directly with each other for subtasks.

This is the most common pattern in production systems. The conductor handles task decomposition and final assembly, while specialist agents collaborate on implementation details without routing every message through the conductor.

Broker/Mediator Pattern

A broker agent acts as a message router without understanding the workflow itself. It receives requests, matches them to the most appropriate agent based on capabilities, and routes the response back.

This pattern is useful when you have a large pool of interchangeable agents and want to balance load or route based on availability. The broker does not plan or reason about the workflow — it simply connects requesters with providers.

Workflow-Based Orchestration

The orchestration logic is defined externally as a workflow graph (a DAG or state machine) rather than being embedded in an agent. Agents are invoked at specific nodes in the graph, and transitions between nodes are determined by the workflow engine.

Strengths:

• Workflow logic is explicit and auditable
• Easy to modify without changing agent code
• Well-suited for regulated environments that require deterministic paths

Weaknesses:

• Less adaptive — agents cannot dynamically alter the workflow
• Requires upfront workflow design
• May not handle unexpected situations gracefully

Model Context Protocol (MCP)

As agentic systems grow, they need to interact with an increasing number of external tools, data sources, and services. The Model Context Protocol (MCP) is an open standard introduced by Anthropic to solve this interoperability challenge.

The Problem MCP Solves

Without a standard protocol, every AI tool integration is custom-built. If you have 5 AI models and 10 external tools, you need 50 custom integrations. Adding a new model means building 10 more. This does not scale.

MCP standardizes how AI models access external tools and data, much like USB standardized how computers connect to peripherals. With MCP, any compliant model can connect to any compliant tool through a single protocol.

How MCP Works

The MCP architecture follows a client-server model:

• MCP Client — Built into the AI application (e.g., Claude Code, Cursor). It discovers available servers, negotiates capabilities, and routes tool calls.
• MCP Server — Acts as a bridge between the AI model and an external system. Each server exposes a defined set of tools and resources. For example, a GitHub MCP server might expose tools for creating pull requests, reading issues, and searching repositories.
• Transport layer — MCP supports multiple transport mechanisms (stdio, HTTP with SSE) so servers can run locally or remotely.

A typical interaction flow:

1. The AI application connects to one or more MCP servers at startup
2. The MCP client queries each server for its available tools and resources
3. When the agent needs to use a tool, the client sends a structured request to the appropriate server
4. The server executes the operation and returns the result
5. The client passes the result back to the agent

Why MCP Matters

MCP brings several practical benefits to multi-agent systems:

• Write once, use everywhere — A tool integration built as an MCP server works with any MCP-compatible AI model
• Community ecosystem — The open standard enables a growing ecosystem of shared MCP servers for common services
• Security boundaries — MCP servers can enforce their own authentication, authorization, and rate limiting
• Local and remote — Servers can run on the developer's machine for low-latency access or remotely for shared team tools

Interoperability and DRY Principles

Multi-agent systems benefit enormously from the same engineering principles that make traditional software maintainable.

Do Not Repeat Yourself (DRY)

When multiple agents need the same capability — say, reading a database or calling an API — that capability should exist in one place. In practice, this means:

• Shared tool libraries — Build common tools once and make them available to all agents through a standard interface (like MCP)
• Centralized configuration — Store shared settings (API endpoints, credentials, model parameters) in a single location
• Reusable prompt components — Extract common instruction patterns into shared templates that multiple agents include

Standardize Communication

Agents that communicate through well-defined protocols are easier to develop, test, and replace. When Agent A sends a code review request to Agent B, the message format should be consistent and documented. This allows you to swap Agent B for a different implementation without changing Agent A.

Design for Replaceability

Build agents so that any individual agent can be replaced without redesigning the system. This means:

• Clear interfaces between agents
• No hidden dependencies or shared mutable state
• Well-documented input/output contracts

When a better model comes along or a specialist agent needs to be rewritten, the change should be localized to that agent alone.

What Comes Next

With an understanding of individual agents and how they coordinate in multi-agent systems, the next chapter shifts focus to the practical frameworks and methodologies for putting these concepts into practice in real development teams.