Your anti-hallucination demos are impressive. GraphRAG returns precise answers. Semantic tool selection cuts errors. Guardrails block invalid operations. Multi-agent validation catches fabricated data. But they all run on your laptop with hardcoded API keys and in-memory data. Production is a different world. The gap is real. Production needs secure credential storage, scalable databases, semantic tool routing without custom FAISS indexes, business rules that change without redeployment, and observability to know when things go wrong. Most teams spend months bridging this gap. Some never do. I will walk through how 5 anti-hallucination techniques translate from prototype to production, including semantic tool routing via MCP (replacing custom FAISS indexes), database-backed steering rules that let you change agent behavior without redeploying, STEER messages that let agents self-correct instead of hard-failing on rule violations, GraphRAG with a managed graph database using auto-built knowledge graphs from 300 documents, and a live demo covering 8 test scenarios including hallucination attempts, rule violations, and edge cases. You'll walk away with: • Complete production architecture deployable as infrastructure-as-code • Database-backed steering rules pattern (change rules in seconds, no redeploy) • STEER message pattern for agent self-correction instead of hard failure • MCP semantic routing replacing custom vector indexes • Open-source code with serverless infrastructure, database tables, and graph database integration Most production AI talks focus on infrastructure and scaling. This one specifically focuses on keeping anti-hallucination guarantees when you move from prototype to production. You will see real steering rules, real STEER messages, and real test scenarios proving the agent does not hallucinate in production either.

Outline: • The Prototype-to-Production Gap • Semantic Tool Routing via MCP Gateway • Steering Rules in DynamoDB • GraphRAG in Production • Full Production Test • Resources + Q&A