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🔬 AI Multi-Agent Research System

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Instead of relying on a single LLM call, I designed a multi-agent system where different agents collaborate like a real research team.

System Architecture:

1) Planner → defines research strategy & search queries
2) Searcher → gathers relevant information (offline/online mode)
3) Analyst → extracts insights, patterns, and key findings
4) Writer → generates a structured research report
5) Fact-Checker → verifies and refines final output

Live: https://multi-ai-agent-research-system-mgcp4yp8nekmdeznnf5szg.streamlit.app/

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Always welcome your feedback.

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The separation between Writer and Fact-Checker is the most important design choice here, and the same principle applies to agent-to-agent communication in professional matching. When I built Opportunity Skill's outreach module, I forced the contacting agent to provide two separate parameters, one for the proposal and one for the benefits to the recipient. Without that structural constraint, agents default to generic pitching that sounds fine but transfers no decision-ready information. Your five-role split works for the same reason. Each agent has a narrow responsibility and a defined output contract. The moment you let one agent do everything end to end, quality collapses. Structure is what makes multi-agent systems produce better output than a single call, not just more output.

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DANISH ZULFIQAR
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AI Engineer | 3 Years Building Production-Grade AI Systems

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