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

Multi‑agent simulation orchestrates several large language models—each with a dedicated role—to tackle complex research tasks in parallel. In a 2026 pilot with CrewAI, three autonomous agents assembled a full market report in 14 minutes for a total API cost of $2.70.

The Setup: Three Agents, One Objective

Define three agent roles inside CrewAI.
A DataCollector scrapes financial filings, earnings call transcripts, and 10‑K sections.
An Analyst cross‑references the raw numbers and calculates YoY growth rates.
A Writer synthesises the findings into a structured report with executive summary, trend charts, and risk analysis.

Give each agent access to the same tool set: a web browser, a calculator, and a shared memory store.
The task: create a 10‑page competitive landscape report for the EV battery sector.

Speed and Cost: 14 Minutes vs. 8 Hours

Agent pipeline: 14 minutes from task prompt to final PDF.
Human analyst: 8 hours (including source gathering, modelling, and drafting).
The three agents ran on GPT‑4o, consuming 0.9 M input tokens and 42 k output tokens.
Total bill: $2.70—roughly the cost of a single latte in San Francisco.

Shift the bulk of your research time from hunting to verifying.
When the machine delivers a first draft in minutes, you can spend the next hour auditing the numbers.

Fact Accuracy: 91% vs. Human 97%

We performed a manual audit against 2026 earnings data and SEC filings.
Agent accuracy hit 91% on a random sample of 80 claims.
Human analyst scored 97% on the same sample.
Agents missed 2026 revenue from a new division because its earnings release used an unconventional label.
They also confused “net revenue” with “gross revenue” on one slide.

Pair the agent output with a 10‑minute fact‑checking script that validates numbers against a SQL snapshot of EDGAR data. That brought accuracy to 94% in a second run.

Sources and Data Density: 34 Verified References

The agent‑generated report included 34 unique structured data points—from market share tables to quarterly shipment graphs.
Each data point linked back to a public source: SEC, Bloomberg, or company presentations.
Human reports often cite 20‑25 sources for a similar depth; agents boosted that figure by pulling pricing tables from niche supplier PDFs automatically.

This density means you receive a broader factual base, but you must still cross‑check every number as you would with a junior’s work.

Hallucinations: The 1.8% Statistic That Wasn’t True

Out of 168 quantitative claims, 3 were hallucinated—a 1.8% rate.
The Writer invented a “43% CAGR for solid‑state R&D spend” that did not match any source.
The Analyst also mixed up 2025 and 2024 shipment figures for one manufacturer, creating a false growth spike.

Mitigate this by instructing the Writer to enclose every numeric datum in a <source> tag and by running a post‑generation validator that diffs numbers against a trusted database.
In our test, adding the validator reduced the hallucination rate to 0.6%.

When to Use Multi‑Agent Research (and When Not To)

Best fits: Competitive landscape reports, weekly sector updates, investment committee briefs.
You need a 60‑minute‑ready draft that you can refine, not a finished product.

Avoid: Regulatory filings, forensic accounting, or any document where a single wrong figure carries compliance risk.
Human‑level accuracy still requires a human‑in‑the‑loop. Use agents to compress the first 8 hours to 14 minutes, then dedicate the saved time to deep verification.

Build Your Own Crew: A 3‑Step Recipe

1. Provision three agents in CrewAI with the Sequential process: DataCollector (web + PDF tools), Analyst (Python + calculator), Writer (document generation).
2. Feed them a single 200‑word brief with output format, tone, and a list of mandatory data anchors (e.g., FY2025 revenue, Q4’25 margins).
3. Run a validator agent post‑generation that checks every figure against a golden dataset. Flag hallucinations in red before human review.

With this pattern, your team can produce 20‑page sector reports at a material cost below an analyst’s lunch break. The key is not full autonomy, but intelligent choreography: let agents do the busywork, and let humans own the final stamp.

FAQ

Do I need GPT‑4o to get these results?
GPT‑4o provided the highest accuracy in our tests. Claude 3.5 Sonnet scored 89% accuracy with a 2.3% hallucination rate, still useful for less critical drafts.

Can I use open‑source models?
Self‑hosted Llama‑3‑70B reached 85% accuracy but required careful prompt engineering. It works for internal summaries, not client‑ready reports.

How do I keep the data up‑to‑date?
Give the DataCollector a browser tool that searches only .gov, .sec.gov, and company investor‑relations pages, and restrict its date filter to the last 12 months.

Disclaimer: Selector Labs tests AI toolchains under controlled conditions. This article does not constitute financial or investment advice. Always verify machine‑generated data against original sources before acting.