Building responsible AI agents requires more than just functional code. While LLMs can rapidly prototype applications, deploying them in enterprise environments demands robust governance, security, and testing frameworks. Traditional software follows deterministic logic, but AI agents rely on probabilistic LLMs that can be manipulated through jailbreaks, sycophancy, or indirect injections. To address this, enterprises must implement Defense in Depth strategies with deterministic safety rails around non-deterministic cores.

This article explores a production-ready agentic RAG architecture using an HR Policy Assistant. Three user personas—Admin (acl_level=2), Bob the HR Manager (acl_level=1), and Alice the Employee (acl_level=0)—demonstrate multi-level access control. The system features a Safety Pre-Filter that blocks injection attacks, a Policy Engine classifying queries into Autonomous, Supervised, or Requires HITL tiers, and hierarchical ACL enforcement at both document and action levels.

Additional safeguards include SHA-256 integrity verification for vector store chunks, preventing tampering detection, and a Safety Post-Filter defending against indirect prompt injections. Every user query passes through multiple validation layers before reaching the isolated LLM core, ensuring compliance with organizational policies and minimizing security risks in enterprise AI deployments.

The architecture emphasizes that building functional AI agents is straightforward, but making them production-ready requires comprehensive security infrastructure.