AI agents hallucinate statistics, pick wrong tools, ignore business rules, and claim success when operations fail. This repository provides 6 hands-on demos — from research-backed local experiments to production deployment on AWS — showing how to detect, prevent, and self-correct these failures using Graph-RAG, semantic tool filtering, multi-agent validation, neurosymbolic guardrails, Agent Control steering, and Amazon Bedrock AgentCore for production deployment.
Built with Strands Agents. Similar patterns can be applied in LangGraph, AutoGen, CrewAI, or other agent frameworks.
Complexity note: Demos 01-05 run locally with minimal setup. Demo 06 requires familiarity with AWS CDK and core AWS services. New to CDK? Start with the CDK Workshop first.
| # | Demo | What It Solves | Key Result | Stack |
|---|---|---|---|---|
| 01 | Graph-RAG vs RAG | Fabricated statistics, incomplete retrieval, out-of-domain hallucination | 73% fewer hallucinations with knowledge graphs |   |
| 02 | Semantic Tool Selection | Wrong tool picks, token waste at scale (29 tools) | 89% token reduction, higher accuracy |  |
| 03 | Multi-Agent Validation | Undetected hallucinations, fabricated responses | Executor-Validator-Critic cross-check pipeline |  |
| 04 | Neurosymbolic Guardrails | Agents ignoring business rules in prompts | Symbolic rules enforced via lifecycle hooks |  |
| 05 | Agent Control Steering | Hard-blocking stops the task instead of fixing it | Agent self-corrects instead of failing |  |
| 06 | Amazon Bedrock AgentCore Production | Taking all techniques to production on AWS | Full deployment with CDK, Lambda, DynamoDB, Amazon Bedrock AgentCore |    |
Each demo builds on the previous one. You can run any demo independently, but the learning path is designed to be progressive:
Phase 1 — Understand Failures: Demos 01-02 show why agents fail — RAG hallucinations and tool selection errors backed by academic research.
Phase 2 — Detect & Prevent: Demos 03-04 show how to catch failures — multi-agent validation and symbolic rule enforcement.
Phase 3 — Self-Correct & Deploy: Demo 05 shows how to steer agents to self-correct instead of blocking (Agent Control), and Demo 06 shows how to ship all techniques to production on AWS (Amazon Bedrock AgentCore, DynamoDB, Lambda).
- Python 3.9+
- uv package manager
- OpenAI API key (or any Strands-supported provider: Amazon Bedrock, Anthropic, Ollama)
cd 01-faq-graphrag-demo # or any demo folder
uv venv && uv pip install -r requirements.txt
# Create .env with your API key
echo "OPENAI_API_KEY=your-key-here" > .env
# Run the demo
uv run <main_script>.py
# Or open the .ipynb notebook in your IDE (VS Code, Kiro, or any editor with notebook support)Each demo README has specific setup instructions and prerequisites.
This repository addresses four main categories: (1) fabricated statistics — when RAG agents guess numbers instead of computing them, (2) wrong tool selection — when agents pick inappropriate tools from large toolsets, (3) business rule violations — when agents ignore constraints expressed only in prompts, and (4) undetected failures — when single agents claim success without validation.
Yes. The patterns (Graph-RAG, semantic tool filtering, multi-agent validation, neurosymbolic guardrails, steering controls) are framework-agnostic concepts. These demos use Strands Agents, but the same approaches apply in LangGraph, AutoGen, CrewAI, Haystack, or custom implementations. The key insight is architectural, not framework-specific.
Only demo 06 (production deployment) requires an AWS account. Demos 01-05 run entirely locally with just Python and an OpenAI API key (or any supported LLM provider).
Demos 02-05 run in under 5 minutes. Demo 01 has a lite mode (30 docs, ~15 minutes) and full mode (300 docs, ~2 hours) for building the knowledge graph. Demo 06 deployment takes ~20 minutes with CDK.
All demos default to OpenAI GPT-4o-mini but work with any provider supported by Strands Agents: Amazon Bedrock (Claude, Titan), Anthropic API, Ollama (local models), or any OpenAI-compatible endpoint. See Strands Model Providers for configuration.
OpenAI API errors: Verify your OPENAI_API_KEY is set in a .env file. Get one at platform.openai.com/api-keys.
Neo4j connection fails (demo 01): Install Neo4j Desktop with the APOC plugin enabled, then restart.
OpenTelemetry warnings: "Failed to detach context" warnings in demos 03-05 are harmless and do not affect functionality.
Model alternatives: Change the model in any demo by modifying the OpenAIModel() constructor. See Strands Model Providers for all supported options.
For demo-specific issues, check the troubleshooting section in each demo's README.
If you discover a potential security issue in this project, notify AWS/Amazon Security via the vulnerability reporting page. Please do not create a public GitHub issue.
This library is licensed under the MIT-0 License. See the LICENSE file for details.
Last updated: March 2026 | Strands Agents 1.27+ | Python 3.9+