Understand what a Multi-Agent System is and when to use one

Understand multi-agent systems through a supervised research-and-review example

What Is a Multi-Agent System? 

A Simple AI Teamwork Example

A multi-agent system uses two or more AI agents that coordinate their work toward a shared objective.

Think of it as an AI project team. One agent may plan the assignment, another may gather evidence, and a third may review the findings. A coordinator combines their work before a person approves the final result.

The important word is not “multiple.” It is “coordinated.”

Adding more agents without distinct responsibilities can produce duplicated work, missing evidence, unnecessary tool use, and conflicting conclusions.

How Is It Different From One AI Agent?

A single agent can plan, use approved tools, and complete several steps on its own. For many tasks, that is enough.

OpenAI recommends maximizing a single agent's capabilities before introducing a multi-agent design because additional agents increase complexity and overhead. Multiple agents may become useful when instructions contain too many conditional branches or one agent repeatedly selects the wrong tools. OpenAI

A multi-agent system divides the work among agents with separate instructions, tools, or areas of responsibility.

Approach How It Works Best Starting Point
Single Agent One agent handles planning, tool use, and task completion. Start here when one agent can complete the task reliably.
Manager Pattern One coordinator delegates work to specialized agents and combines their findings. Useful beginner model when specialized workstreams are justified.
Handoff Pattern Agents transfer control when another specialization is needed. Useful when different stages require different specialists.

For a beginner trial, the manager pattern is usually easier to understand because one coordinator retains control of the overall assignment.

A Simple Research-and-Review Example

Imagine that a small-business owner wants to compare three approved appointment-scheduling tools.

The AI team could contain several clearly separated roles.

1. Coordinator Agent

The coordinator receives the task brief and divides the assignment into clear parts.

It defines:

  • Approved product pages
  • Required comparison fields
  • Output format
  • Verification date
  • Stop conditions
  • Information requiring human review

2. Research Agent

The research agent opens only the approved sources and records documented features, prices, and limitations.

It must attach a source URL to every material claim and mark unavailable information as “Needs verification.”

3. Review Agent

The review agent compares the research notes with the approved sources.

It checks for:

  • Unsupported statements
  • Lost qualifications
  • Conflicting information
  • Missing citations
  • Inconsistent dates
  • Claims that extend beyond the evidence

Important: The reviewer agent should not silently “correct” missing facts by inventing replacements. Missing evidence should remain visible.

4. Coordinator + Human Reviewer

The coordinator receives the research and review outputs, resolves only the issues it is authorized to handle, and prepares one comparison.

A person then verifies the evidence and approves, revises, or rejects the final result.

A SIMPLE MULTI-AGENT WORKFLOW

TASK → COORDINATOR → RESEARCH → REVIEW → COORDINATOR → HUMAN APPROVAL

AI agents should not approve their own consequential recommendations.

Why Use Specialized Agents?

A multi-agent system may help when a task has several genuinely distinct workstreams.

Anthropic describes a research system in which a lead agent delegates separate research assignments to parallel subagents before synthesizing their findings. It also notes that multi-agent systems introduce additional challenges involving coordination, evaluation, and reliability. Anthropic

Useful candidates include:

  • Researching several independent topics
  • Comparing information from different source groups
  • Separating drafting from critical review
  • Routing requests to different specialists
  • Processing work that exceeds one agent's practical context
  • Applying different tools or policies to separate task categories

THE BENEFIT MUST COME FROM THE DIVISION OF LABOR — NOT MERELY FROM INCREASING THE NUMBER OF AGENTS.

Define Every Handoff

A handoff is the information one agent passes to another. This is one of the most important parts of a multi-agent workflow.

A useful handoff should state:

Handoff Field What It Should Show
Completed Task What the agent actually completed
Evidence Facts and supporting material collected
Sources Where important claims came from
Open Questions Information still unresolved
Verification Status What has and has not been checked
Output Format Structure expected by the next agent
Next Step What should happen next
Escalation Conditions requiring human review

Without a structured handoff, evidence can disappear between agents. A polished final answer may then hide weak or unsupported intermediate work.

Copy-and-Paste TEAM Prompt

Use the following prompt to design a supervised multi-agent workflow.

Design a supervised multi-agent workflow for the task below. Begin by determining whether one agent could complete the task reliably. If multiple agents are justified, define one measurable responsibility for each agent. Specify the coordinator, specialist and reviewer roles; approved inputs and tools; required handoff fields; acceptance criteria; stop conditions; operating limits; and human-approval points. Do not assume access, authority or facts that are not listed. Prevent duplicate work by assigning non-overlapping scopes. Require source links for factual claims and mark missing evidence “Needs verification.” Keep publishing, purchasing, messaging, sensitive-data transmission and production changes subject to human approval.

Goal: [DESCRIBE THE OUTCOME]
Approved inputs: [LIST SOURCES OR DATA]
Allowed tools: [LIST TOOLS]
Prohibited actions: [LIST ACTIONS]
Human owner: [ROLE]
Operating limits: [ADD STEP, TIME OR COST LIMITS]

TEAM

TASK → EXPERT ROLES → APPROVED HANDOFFS → MONITORED EXECUTION

Five Common Multi-Agent Mistakes

1. Giving Every Agent the Same Assignment

Overlapping instructions create duplicate work instead of useful specialization.

2. Using Vague Roles

“Research agent” is incomplete. Define the approved sources, required fields, boundaries, and completion criteria.

3. Losing Evidence During Handoffs

Require structured outputs containing claims, sources, verification status, and unresolved questions.

4. Omitting Stop Conditions

Set maximum steps, retries, tool calls, or runtime. Define when the system must pause or escalate.

5. Evaluating Only the Final Writing

A readable answer can conceal poor routing or unsupported research. Review both the final outcome and the evidence trail.

Keep the Complete System Supervised

Each agent requires appropriate permissions. A research agent may need read-only access, while a review agent may need no external tools at all.

Do not provide every agent with every tool.

Give each agent only the access required for its defined responsibility. Keep consequential actions behind appropriate approval gates.

Remove confidential, personal, financial, medical, employee, and client information unless an approved system and documented policy permit its use. Require qualified human review for consequential decisions.

NIST's Generative AI Profile provides voluntary guidance for incorporating trustworthiness considerations into the design, use, and evaluation of generative-AI systems. NIST

Before You Build a Multi-Agent Workflow

✓ Could one agent complete the task reliably?
✓ Does every additional agent have a distinct responsibility?
✓ Are approved tools and sources clearly defined?
✓ Are handoff fields standardized?
✓ Can factual claims be traced to evidence?
✓ Are duplicate responsibilities prevented?
✓ Are stop conditions and operating limits defined?
✓ Are consequential actions held for human approval?

Conclusion

A multi-agent system is an AI team—not a guarantee of a better answer.

Use one agent when one agent can complete the work reliably. Add another only when it has a distinct, measurable responsibility that improves the complete workflow.

Define the goal, roles, tools, handoffs, review criteria, and stop conditions. Preserve the evidence and retain human approval for consequential actions.

DEFINE → DELEGATE → HANDOFF → VERIFY → COORDINATE → HUMAN APPROVAL

MORE AGENTS DO NOT AUTOMATICALLY MEAN BETTER AI. CLEAR ROLES, EVIDENCE AND CONTROL MATTER MORE.

Next Step

Download the Multi-Agent System Checklist + TEAM Prompt Sheet and map one supervised research-and-review trial.

Continue learning: Human-in-the-Loop AI: When Should AI Ask for Help or Approval?

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