5 Techniques to Stop AI Agent Hallucinations in Production

AI agent hallucinations occur when an LLM-powered agent fabricates data, selects the wrong tool, or ignores business rules during autonomous task execution. This post deploys 5 production-ready techniques to stop them — using managed hosting, serverless tools, database-driven guardrails, semantic tool routing, and a knowledge graph. Everything deploys as infrastructure as code. TL;DR — 5 techniques, one production stack: Graph-RAG on Neo4j AuraDB eliminates fabricated aggregations with Cypher queries Semantic tool routing via AgentCore Gateway replaces custom FAISS indexes Multi-agent validation on Lambda + DynamoDB catches errors single agents miss Database-driven steering rules update agent behavior without redeploying Hard hooks + soft steers separate financial constraints from operational adjustments Every demo in this series on stopping AI agent hallucinations ran on a laptop. Hardcoded data, in-memory rules, a single user. The techniques worked, but the infrastructure did not sca