How to Build Transparent AI Agents: Traceable Decision-Making with Audit Trails and Human Gates

This tutorial demonstrates building transparent AI agents with traceable decision-making. The system logs every thought, action, and observation in tamper-proof audit trails while enforcing dynamic permissions for risky operations. By combining LangGraph's interrupt-driven human-in-the-loop controls with hash-linked databases, developers can create AI workflows where decisions remain auditable, controllable, and explicitly human-approved, addressing critical transparency and accountability...