Stop AI Hallucinations With These 5 Techniques

Your AI agent calls a tool, gets an error, and instead of telling you, it generates a confident 'success' response. The user never sees the failure. Here are 5 techniques to stop that. Every time your agent responds, you're paying for tokens going in and tokens coming out. If what goes in isn't right, you're burning money and getting hallucinations back. I walk through 5 techniques to reduce token waste, improve acuracy, and catch failures before your users see them. Each one comes with a before-and-after demo using a travel agent built with Strands Agents (open source framework from AWS), and all the code is in the repo linked below. The 5 techniques: 1. Semantic Tool Selection. Filter which tools go into the context window on every call so the model only sees what it needs. Token usage drops from thousands to fewer than 300. 2. Graph RAG. For precise queries like aggregations, counts, and multi-hop reasoning. The graph runs the query and returns a computed, verifiable answer instead of guessing from retrieved text. 3. Multi-Agent Validation. A second agent checks every response before it reaches the user, so silent failures get caught. 4. Symbolic Guardians (Hooks). Your rules live in Python code, not in the prompt. The model can't skip them. 5. Runtime Guardrails (Agent Control / Steering). When a rule fires, the agent self-corrects and completes the task without blocking the user or requiring retries. I use OpenAI as the model provider in the demos, but you can swap it for Bedrock, Anthropic, or Llama with one line change. At the end, I show you how Amazon Bedrock AgentCore handles all of this at scale: semantic tool selection via the gateway, graph RAG with Neptune, steering rules in DynamoDB, and full observability built in. Read the blog: https://go.aws/42Ptsi5 Github repo: https://go.aws/4x1q41F Morgan's Amazon Bedrock for Beginners video: https://go.aws/4dos2kL Follow AWS Developers! 🆇 X: https://go.aws/3PtYitD 💼 Linked