Multi‑Agent Simulation: Coordinating 3 AI Agents to Research a Market Report

A multi-agent simulation divides a research task among AI agents with different roles. Use one to collect material, one to organise and check the findings, and one to draft the report. A human should review the result before you rely on it.

The Setup: Three Agents, One Objective

Start by defining the agents and their responsibilities. This is a hypothetical setup:

• A Data Collector gathers filings, transcripts, pricing pages, and other relevant material.
• An Analyst compares the material, identifies patterns, and checks calculations.
• A Writer turns the findings into a structured report with an executive summary, charts, and risk notes.

Give the agents access only to the tools they need, such as a web browser, document reader, calculator, or shared notes. Set a shared objective and define what each agent must pass to the next one.

Specify the report’s audience, required sections, tone, and evidence standards. Tell the agents to mark missing information instead of filling gaps with assumptions.

Dividing the Work

Assign collection, analysis, and drafting to separate roles. This helps you see where a problem enters the workflow and makes human review more manageable.

Use a sequential process when later steps depend on earlier ones. You can also allow limited parallel work when the agents can operate independently.

Keep the final task with a person. The agents can produce a first draft, but you should approve the conclusions, wording, and source checks.

Reviewing the Draft

Check every number against the original source. Confirm that dates, definitions, units, and company names are consistent across the report.

Watch for common errors, including:

• mixing net and gross figures;
• confusing reporting periods;
• attributing a claim to the wrong company;
• treating a forecast as an established fact;
• repeating a figure without preserving its context;
• using a source that does not support the claim.

Ask the agent to show its source for each material assertion. If it cannot provide a source, remove the assertion or mark it as unverified.

Handling Missing or Uncertain Information

Tell agents not to invent citations, data points, quotations, or calculations. Require them to label gaps clearly and explain what additional information is needed.

Use a separate verification pass. A reviewer can check citations, compare figures with source documents, and flag contradictions before the report is shared.

Do not treat a polished explanation as evidence. The more confident the wording sounds, the more carefully you should inspect the underlying claim.

When to Use Multi-Agent Research

A multi-agent workflow can help with competitive landscape drafts, recurring sector updates, and internal briefing materials. It is most useful when you need an organised starting point rather than a finished report.

Avoid using it on its own for regulatory filings, forensic accounting, investment decisions, or other work where an incorrect figure could cause serious harm. Keep a qualified person involved in those tasks.

Use the agents to reduce repetitive work. Keep human judgment over priorities, interpretation, risk, and final approval.

Build Your Own Crew

1. Define the agents. Assign collection, analysis, writing, or verification roles. Give each agent a clear input, output, and boundary.
2. Write a focused brief. State the report’s purpose, audience, structure, tone, required evidence, and handling rules for missing information.
3. Run a validation pass. Check material claims against a trusted dataset or the original source. Flag unsupported figures before human review.
4. Review the final draft. Confirm that the conclusions follow from the evidence and that uncertain material is labelled clearly.

Do not aim for full autonomy. Let agents handle repetitive preparation, and let a person own the final decision.

Questions to Ask Before You Rely on the Output

• Which sources support each important claim?
• What information was missing from the input?
• Which figures were calculated rather than copied directly?
• How were conflicting sources handled?
• Which conclusions are interpretations rather than reported facts?
• What requires human approval before publication?

FAQ

Do I need a particular model?
Choose tools that fit the task, your privacy requirements, and your review process. Compare their capabilities using your own documents and acceptance criteria.

Can I use open-source tools?
You can, but consider setup, maintenance, security, and support before adopting one for business work. Test it with non-sensitive material first.

How do I keep the data current?
Restrict collection to approved source types, record the date of retrieval, and require agents to flag outdated or conflicting information. Recheck key claims before publication.

Always verify machine-generated data against original sources before acting.