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# Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 55 min read · 1 upvotes · 0 comments

## Summary

Five code-level techniques to reduce AI agent hallucinations, demonstrated using the Strands agent framework with a travel agent example. The techniques are: (1) semantic tool selection — dynamically filtering which tools enter the context window using vector search to cut token usage from ~3,000 to under 300; (2) GraphRAG — replacing vector retrieval with Neo4j Cypher queries for aggregation and counting tasks that require precise, verifiable answers; (3) multi-agent validation — using a Strands Swarm of executor, validator, and critic agents to catch fabricated success responses; (4) neuro-symbolic guardians — enforcing business rules (max guests, payment before confirmation, date validation) in Python hooks that fire before tool execution, making them impossible for the model to bypass; and (5) runtime steering — using an agent-control server to register soft rules that redirect the agent (e.g., split a 50-guest booking into multiple rooms) rather than hard-blocking. Production deployment via Amazon Bedrock Agent Core is also covered, providing managed runtime, gateway, memory, and observability without self-managed infrastructure.

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Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#amazon-bedrock](https://daily.dev/tags/amazon-bedrock), [#neo4j](https://daily.dev/tags/neo4j)

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