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Build Your First Multi-Agent Workflow - See how Graph Engineering helps AI agents work together

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The video demonstrates how to use planner agents, parallel researchers, and skeptic reviewers to independently analyze data and find gaps before merging into a final output. You will see a practical demonstration using Claude to solve a complex marketing problem, proving how specialized AI agents significantly reduce hallucinations and improve reliability.

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Hi Product Hunt 👋 This video is a practical deep dive into Graph Engineering. My first workflows looked reasonable, but the AI was doing too much at once. The same model researched, analyzed, and made decisions simultaneously, which caused hallucinations and missed critical constraints. So I rebuilt the workflow using five rules of Graph Engineering: - Break complex problems into independent planner jobs - Run parallel specialist agents for specific research - Introduce a skeptic agent to find gaps and contradictions - Use a merger to compile surviving evidence into one output - Build an orchestrator to automate reports and emails I then ran the same marketing campaign task using Claude, dividing the workload across specialized agents with clear constraints. Both runs used the same source files. The multi-agent output was much more reliable, correctly catching margin limits and stock issues that a single-prompt approach completely overlooked. There is also one advanced tip about connecting these agents through Make to create a fully automated one-click workflow. Curious to hear from you: which automation tool has made the biggest difference in your own multi-agent workflows?