How AI agent identity risk creates privilege escalation, tool misuse, and audit gaps — and how runtime controls, credential vaulting, and approval gates reduce exposure.
AI agent prototypes fail in production because they get tools before they get boundaries. A CTO guide to runtime controls, credential vaulting, approval gates, audit trails, and deployment topology.
Enterprise AI agents don't fail first at reasoning. They fail at control. Five hard lessons from building the governed layer between agents and enterprise systems.
Why most enterprise AI agent deployments fail before production — and the six-step framework that gets them there. Security, auditability, and data residency covered in depth.
Most enterprises don't fail at AI because the model is weak. They fail because the agent can't be trusted around real systems. That problem is harder in regulated environments.
How we structure the trace that answers 'what exactly did the agent do and why?' — from input ingestion through model calls to connector outcomes.
On-premise isn't a checkbox. It changes how you think about credential management, audit log storage, connector security, and the control plane from the ground up.