Case Study
Agentic workflow system where specialized AI agents collaborate to research topics, gather data, cross-verify facts, and produce comprehensive reports. Role-based agent teams with shared memory.
4 specialized agents coordinated via LangGraph
85% fact-check agreement rate
Reports generated in <5min
Research tasks require gathering information from multiple sources, verifying facts, and synthesizing findings — a process that's tedious and error-prone when done manually.
Role-based agent teams: Researcher, Fact-Checker, Writer, Editor
LangGraph state machine for agent coordination and handoffs
Shared Redis memory store for cross-agent context
Web scraping and API integration for multi-source data gathering
Cross-verification pipeline that flags conflicting information
Structured report output with citations and confidence scores
Agent coordination was the hardest part — designed a state machine that handles failures, retries, and escalation between agents
Built shared memory with scoped access — agents can read shared context but write to their own namespace to prevent conflicts
Cross-verification required comparing information from structurally different sources — used embedding similarity to match claims across formats
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