Your anti-hallucination demos look impressive on your laptop. GraphRAG returns precise answers, guardrails block invalid operations, validation catches fabricated data. Then you ship, and the notebook falls apart: hardcoded keys, in-memory data, no observability, a custom FAISS index nobody wants to maintain. The hard part of agent reliability is not the technique. It is making the technique survive production. This talk shows how 5 anti-hallucination techniques translate from prototype to production: semantic tool routing via MCP (no custom vector index), database-backed steering rules you change in seconds without redeploying, STEER messages that let agents self-correct instead of hard-failing, and GraphRAG on a managed graph database built from 300 documents. A live demo runs 8 scenarios including hallucination attempts and rule violations. You'll walk away with: • A complete production architecture deployable as infrastructure-as-code • Database-backed steering rules you change in seconds, no redeploy • The STEER message pattern for self-correction instead of hard failure • Open-source code with serverless infrastructure and graph database integration

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