Build a Multi-Agent Research Assistant
Chain together multiple AI models — each with a specialized role — to conduct deep research on any topic, fact-check findings, and produce a structured report automatically.
Time Required
4 hrs setup / 20 min per report
Expected Result
A structured, fact-checked research report on any topic, produced in 20 minutes by a chain of specialized AI agents rather than hours of manual research.
Recommended Tools
Design Your Agent Pipeline
Map out your agent roles: (1) Query Planner — breaks the research question into sub-queries; (2) Search Agent — fetches data from the web; (3) Synthesizer — combines findings; (4) Fact-Checker — validates claims. Use Claude to help design this architecture.
Set Up the Query Planner Agent
Create a Make scenario where Agent 1 (ChatGPT) receives a research question and outputs 5–8 specific sub-queries needed to answer it comprehensively.
Deploy the Search Agent
For each sub-query, trigger a Perplexity search via Make's HTTP module. Perplexity's API returns answers with citations — store each result with its source URL.
Run the Synthesizer Agent
Pass all the gathered sub-answers into Claude with the original question. Claude synthesizes findings into a structured report: Executive Summary, Findings by Section, Key Contradictions, and Source List.
Fact-Check Critical Claims
Extract factual claims from the report and run them through a second Perplexity pass to verify. Claude cross-references the verification results and flags any claims that couldn't be confirmed.
Format and Deliver
Use Make to send the final report to Notion AI for formatting and tagging. Trigger an email notification with a link to the new report page.