A multi-agent system is a software architecture that coordinates multiple AI agents to complete a shared task. Each agent can have a focused role, such as researching, coding, testing, or reviewing, while defined rules control handoffs, shared context, and final decisions. It is most useful when splitting work improves reliability or speed, not simply because multiple agents are available.
Multi-Agent System
What Is a Multi-Agent System?
A multi-agent system is software that coordinates multiple AI agents toward one goal. Each agent can use tools, handle a role, and pass results to others.
How Multi-Agent Systems Work
- Accept a goal.
- Split it into tasks.
- Assign specialist agents.
- Share structured results through messages or memory.
- Validate the output.
- Return or execute an approved result.
An orchestrator, either code or an agent, manages handoffs. Agents can use different models.
Multi-Agent vs. Single-Agent Systems
| Factor | Single agent | Multi-agent |
|---|---|---|
| Scope | One workflow | Split workflow |
| Cost and latency | Usually lower | Usually higher |
| Failures | One reasoning path | Handoff or conflict risks |
| Best fit | Short, coupled tasks | Separable, checkable tasks |
A practical middle ground is one agent with several tools.
Common Multi-Agent Architecture Patterns
| Pattern | Software use |
|---|---|
| Supervisor-worker | Delegate coding and testing. |
| Sequential pipeline | Plan, build, test, deploy. |
| Peer-to-peer | Coordinate equal specialists. |
| Reviewer | Check another agent's output. |
Unrestricted all-to-all communication is harder to test and audit.
Benefits of Multi-Agent Systems
Specialization can improve complex software workflows.
- Parallel work can reduce waiting.
- Independent review can catch errors.
- Separate roles support clearer permissions.
- A coding, test, and security agent can review one feature.
Practical Limits of Multi-Agent Systems
More agents do not automatically produce better results.
- Model calls, tool use, and duplicated context raise cost.
- Slow handoffs and conflicting advice can delay delivery.
- Errors can cascade without logs, task limits, structured outputs, and approval gates.
- Excessive tool permissions create security risk.
When to Use a Multi-Agent System
- Use one when tasks are separable, parallel work matters, and outputs are verifiable.
- Use one when roles need distinct tools or permissions.
- Prefer a single agent for short, low-risk, tightly coupled work.
Multi-Agent Systems in Emergent
Emergent applies agentic software development to architecture, implementation, integrations, testing, and deployment. Learn about AI agents, agentic coding, and building a multi-LLM application.
Frequently Asked Questions
Your Questions, Answered
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Can you give me an example of a multi-agent system?
A feature-development workflow can use a planning agent to define work, a coding agent to implement it, a test agent to run checks, and a reviewer agent to assess the change before release.
How do you build multi-agent systems?
Start with a task that can be safely divided. Define each agent's role, tools, inputs, outputs, permissions, handoff rules, logging, and human approval points. Test the workflow with realistic failure cases.
Why do multi-agent LLM systems fail?
They often fail because roles overlap, agents receive inconsistent context, handoffs are vague, or agents have unsafe tool access. Unchecked errors can also pass from one agent to the next.
Is ChatGPT an agent or an LLM?
An LLM is the language model that generates and interprets text. ChatGPT is an AI product built around LLMs. It becomes agent-like when it can pursue goals, use tools, retain relevant context, and act within defined limits.
What are the different types of agents?
Common software agent roles include planner, researcher, coding, testing, reviewer, integration, browser, and deployment agents. Types are usually defined by their job, available tools, and permissions.
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