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

Source: [YouTube](https://www.youtube.com/watch?v=vJukHCIv7Ck)  
Feed7 permalink: https://feed7.dev/p/stop-ai-agent-hallucinations-5-techniques-production-patterns-elizabeth-09g1w9s  
Published: Unknown  
Trust: Source Linked (source_linked)

## Why Included

Five code-level controls reduce agent errors: narrow tool context, query structured data, validate responses, enforce rules before calls, and steer runtime correction.

## Source Summary

The talk demonstrates **5 code-level techniques**: semantic tool selection, graph retrieval, multi-agent validation, pre-call symbolic rules, and runtime steering. Its sample travel agent exposes **29 tools**, all otherwise added to every request.

## Practical Implication

Filter tools per invocation, compute precise counts and multi-hop answers from structured queries, and validate tool results before replying. Put non-negotiable booking or payment constraints in executable hooks, then use steering where an agent can safely correct itself.

## Agent-Ready Context

The talk demonstrates **5 code-level techniques**: semantic tool selection, graph retrieval, multi-agent validation, pre-call symbolic rules, and runtime steering. Its sample travel agent exposes **29 tools**, all otherwise added to every request.

Filter tools per invocation, compute precise counts and multi-hop answers from structured queries, and validate tool results before replying. Put non-negotiable booking or payment constraints in executable hooks, then use steering where an agent can safely correct itself.

These are demo patterns built with Strands and AWS services, not measured guarantees across production workloads. Filtering does not stop conversation history from growing, while validators and graph infrastructure add latency, cost, and operational complexity.

## Context Map

- Layer: agent
- Domains: coding, data
- Topics: agent-reliability, tool-use, harness-engineering

## Uncertainty

- These are demo patterns built with Strands and AWS services, not measured guarantees across production workloads. Filtering does not stop conversation history from growing, while validators and graph infrastructure add latency, cost, and operational complexity.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.