AI agents are becoming increasingly powerful, but in real enterprise environments, power without control is dangerous. Most GenAI demos focus on getting responses, but very few show what happens when an AI agent is exposed to prompt injection, unauthorized tool access, data leakage, or unsafe outputs.
This hands-on Gen AI Project #4 workshop is designed to bridge that gap.
In this workshop, you will build Agent Guardian, a production-grade enterprise AI agent that clearly demonstrates the difference between an unguarded AI system and a secure, guardrail-driven AI system. Using a real backend architecture, you will see how a single configuration change can turn an AI agent from unsafe and exploitable into secure, compliant, and enterprise-ready.
You will work with a modern stack including FastAPI, LangChain, and NeMo Guardrails, and implement security mechanisms that real companies rely on when deploying AI in production. This includes input validation, prompt injection detection, PII identification and redaction, role-based access control (RBAC), secure tool execution, and structured observability logging.
A key highlight of this workshop is the guardrails toggle. You will intentionally run the same AI agent with guardrails turned OFF and ON, observing real differences in behavior. You will see how unguarded AI can expose sensitive data, bypass rules, and misuse tools — and how guardrails stop these failures in real time.
Rather than focusing on theory, this workshop emphasizes practical system design. You will understand not just what guardrails do, but where they are applied in the request lifecycle, how they integrate with tools and APIs, and why they are essential for enterprise adoption.
By the end of this workshop, you will have a complete, working reference project that demonstrates how to build AI agents that are powerful, secure, observable, and ready for real-world deployment.
Building AI that works is easy.
Building AI that is safe, secure, and production-ready is the real skill.