A single AI model can write code, answer questions, search the web, and call APIs. So why would you ever need more than one?
Because some tasks are too complex, too parallel, or too large for a single context window. Multi-agent systems exist to solve those problems — not as a way to add complexity, but as a way to match the right tool to the right scope.
What a multi-agent system is
A multi-agent system is a setup where multiple AI agents work together to complete a task. Each agent has a defined role, a set of tools, and its own context. They coordinate through a shared protocol or direct handoffs.
The simplest version has two parts:
- Orchestrator — the agent that plans the work, breaks it into subtasks, and delegates
- Subagents — the agents that execute specific subtasks and report back
This mirrors how a team works. A project lead does not write every line of code. They break the work down and assign it.
Why use multiple agents
1. Tasks that exceed a single context window
An agent analyzing a 500-file codebase cannot fit everything into one context. A multi-agent setup lets one agent handle architecture decisions while others focus on specific modules, then synthesize the results.
2. Tasks that can run in parallel
If you need to research five topics, one agent doing them sequentially is slow. Five subagents running simultaneously are five times faster.
3. Separation of concerns
Some tasks benefit from specialization. A research agent optimized with web search tools works differently from a coding agent loaded with file access and shell tools. Keeping them separate means each one is better at its job.
4. Error isolation
If a subagent fails or produces bad output, it does not crash the whole system. The orchestrator can retry, reroute, or handle the failure without starting over.
The orchestrator/subagent pattern
This is the most common pattern in production multi-agent systems.
User Request
|
Orchestrator Agent
|
┌──┴──┐
| |
Sub-1 Sub-2 Sub-3
(research) (code) (write)
|
Synthesized ResponseThe orchestrator:
- Receives the task
- Plans the approach
- Spawns subagents with specific instructions
- Collects and merges their outputs
- Produces the final result
Subagents:
- Receive a narrow, specific task
- Execute using their assigned tools
- Return structured output
The key is that subagents do not need to know about the big picture. They just do their job and return results.
Claude Agent Teams
Claude has a native implementation of this pattern called Agent Teams. It is available through Claude Code and the Claude API.
With Agent Teams, you define:
- A primary (orchestrator) agent with a high-level goal
- One or more subagents with specific personas, tools, and instructions
The orchestrator can spawn subagents dynamically based on what the task needs. Subagents can be ephemeral (created for one task) or persistent (reused across sessions).
A simple Claude Agent Teams config looks like:
# Primary agent
name: project-lead
description: Coordinates research and writing tasks
subagents:
- researcher
- writer
# Subagents
name: researcher
tools: [web_search, read_file]
instructions: "Search for accurate, up-to-date information. Return structured summaries."
name: writer
tools: [write_file]
instructions: "Write clear, concise content based on provided research. Match the site's tone."When to use multi-agent vs single agent
| Situation | Use |
|---|---|
| Simple Q&A or content generation | Single agent |
| Task fits in one context window | Single agent |
| Sequential steps with no parallelism | Single agent |
| Task exceeds context window | Multi-agent |
| Steps can run in parallel | Multi-agent |
| Different tools needed for different steps | Multi-agent |
| Long-running autonomous workflows | Multi-agent |
A common mistake is reaching for multi-agent architecture when a single well-prompted agent would do. Add agents when you have a clear reason — not by default.
Real use cases
Code review pipeline: An orchestrator takes a PR, spawns a security agent, a style agent, and a logic agent in parallel, then synthesizes their feedback into a unified review.
Research report generation: An orchestrator breaks a topic into subtopics, spawns parallel research agents for each, collects summaries, and a writer agent compiles the final report.
Automated QA: An orchestrator reads a spec, spawns agents to write unit tests, integration tests, and E2E tests, then runs them and reports failures.
Customer support triage: An orchestrator reads a ticket, spawns a classifier agent and a knowledge-base agent, then routes to the right response template.
What to watch out for
Token cost compounds. Every subagent uses tokens. A system with five agents running in parallel uses 5x the tokens of a single agent. Plan accordingly.
Coordination overhead. The orchestrator’s planning and synthesis add latency. For simple tasks, this overhead is not worth it.
Error propagation. If the orchestrator’s plan is bad, all subagents execute the wrong thing. Invest in orchestrator prompt quality.
Context fragmentation. Subagents do not share memory by default. Design explicit handoffs so critical context does not get lost between agents.
Next steps
- LangGraph vs CrewAI vs Claude Agent Teams: Which Should You Use? — framework comparison
- What Are Claude Agent Skills and How Do They Work? — extend agents with skills
- Claude Agent Teams docs in the Claude Code documentation
Related Reading.
What Are Agent Skills? AI Tools Explained Simply
Agent skills are the actions an AI can take beyond just talking. Learn what skills are, how they differ from prompts, and why they make AI actually useful in real workflows.
LangGraph vs CrewAI vs Claude Agent Teams: Which Should You Use?
A practical comparison of the three dominant multi-agent frameworks in 2026 — LangGraph, CrewAI, and Claude Agent Teams — with a decision table to pick the right one.
Vercel AI SDK Tools: One API for Claude and OpenAI Skills
Vercel AI SDK's unified tool interface works with Claude, OpenAI, and Gemini. Write your skill once and switch AI providers without rewriting the agent loop.