AI agents that book 15 guests in a 10-person room. Agents that fabricate statistics when the data doesn't exist. Agents that pick the wrong tool from 29 options and burn tokens. These aren't prompt engineering failures. They're architectural limitations that need structural solutions. This hands-on workshop covers 5 research-backed techniques. Graph-RAG replaces vector similarity guessing with precise entity relationships, cutting fabricated statistics by 73%. Semantic tool selection filters 29 tools to the relevant 5, for an 89% token reduction. Multi-agent Executor-Validator-Critic swarms catch 92% of fabrications. Neurosymbolic guardrails enforce rules through lifecycle hooks agents cannot bypass. Agent steering guides agents to self-correct instead of hard-failing. Each demo includes live code, before and after metrics, and a final module on production deployment. You'll walk away with working Python implementations, a decision framework for each technique, and an open-source repository adaptable to your domain. No AWS account or cloud costs required. Sandboxes are provided.

Outline: • Introduction - Why AI Agents Hallucinate Differently Than LLMs • Demo 00 - Strands Agents Primer • Demo 01 - Graph-RAG vs. Standard RAG • Demo 02 - Semantic Tool Selection • Demo 03 - Multi-Agent Validation • Demo 04 - Neurosymbolic Guardrails • Demo 05 - Agent Control Steering • Demo 06 - Production on Amazon Bedrock AgentCore • Workshop Recap + Resources • Q&A