Prasad Bhamidipati
THE FRAMEWORK

The Case for Agentic Runtime Governance

AI governance has a category error at its foundation. It is not a policy problem — it is an engineering problem. The question is whether your architecture makes governance enforceable at runtime.
01 · RUNTIME VERIFICATION

The Geometry of Trust: Runtime Verification for AI Agents

AI agents need a verification layer between generation and action. Not a guardrail — an architecture. Here is how to build one that works at production scale.
03 · EVIDENCE-BOUND AUTHORIZATION

Evidence Based Authorization

AI agents in production hold standing permissions. Identity confirms who the agent is — not whether any specific invocation, with these parameters and this context, is actually warranted. Evidence-bound authorization fills that gap: every tool call earns its token, or it does not execute.
04 · DECISION PROVENANCE

Decision Provenance: Authority Chains and Human Proximity for AI Agents

AI agents introduce two new dimensions to governance that audit logs have never had to record: the chain of delegation that produced an action, and how close a human was to the decision. Here is the architecture that captures both.