Multi-Agent Orchestration Patterns

Introduction

So far we've used a single AI agent. But modern workflows often need several specialized agents working together. Multi-agent orchestration is the practice of coordinating these agents — each with its own focus — so they cooperate on a complex task without stepping on each other.

Why Multiple Agents?

A single AI model doing everything becomes a bottleneck. Different parts of a workflow benefit from specialized agents:

• Planner — breaks the task into steps and sets the direction
• Coder — writes and refines implementation
• Tester — verifies behavior and catches regressions
• Documenter — produces and maintains documentation

A Typical Feature-Building Pattern

A common pattern for building a feature coordinates agents for planning, coding, testing, and documentation:

Planner  -->  Coder  -->  Tester  -->  Documenter
   |            |            |            |
   +--- task ---+-- code ---+-- verify --+-- docs

The planner produces a spec. The coder implements it. The tester validates and reports failures back to the coder. The documenter captures the final state.

Coordination and Handoff

The core challenge in multi-agent systems is managing context handoff and avoiding conflicting actions. If two agents both edit the same file, they can overwrite each other. If context isn't passed cleanly, the next agent starts blind.

Good orchestration:

• Passes a clear, structured task brief between agents
• Uses version control as the shared source of truth
• Assigns ownership of specific files or concerns to specific agents
• Defines acceptance criteria before an agent hands off

Using MCP in Orchestration

MCP servers make orchestration cleaner. Each agent can connect to the servers it needs, and shared state lives in structured, queryable tools rather than in fragile conversation memory.

• The coder uses filesystem + git servers
• The tester uses browser + git servers
• Everyone reads from the same repository, so handoffs are concrete

Common Failure Modes

• Blind handoffs — no clear spec, the next agent guesses
• Conflicting edits — two agents touching the same code
• Runaway loops — agents ping-pong fixes indefinitely
• Scope creep — losing track of the original goal

Real-World Example

You ask your orchestration to "add a search bar to the dashboard." The planner defines the spec and acceptance criteria. The coder implements it in the frontend and backend. The tester runs the browser server, types a query, and confirms results appear — then reports a minor bug. The coder fixes it. The documenter updates the README. The flow terminates when the tester passes the acceptance criteria.

Summary

• Multi-agent orchestration splits complex tasks across specialized agents
• Common roles: planner, coder, tester, documenter
• Manage context handoff and avoid conflicting edits
• Use MCP servers and version control for shared, concrete state

Next Lesson

As conversations and agents grow, context management becomes critical — let's learn to manage it at scale.