Runtime Governance for AI Agents

Abstract

The central engineering problem in applied AI is no longer whether a model can generate a plausible answer. The problem is whether a system that can retrieve, plan, browse, call tools, and trigger actions can be made governable when mistakes have real cost.

Runtime governance is the discipline that determines what an AI system is allowed to do, under what evidence, with what approvals, under which contextual constraints, and with what audit trail.

Core Architecture

This paper frames runtime governance as part of core system design rather than a compliance overlay. The design target is a bounded decision system with explicit provenance, capability limits, policy checks, isolated execution, approval gates, rollback awareness, and human-verifiable traces.

1. Label inputs by source, trust level, sensitivity, freshness, and transformation history.
2. Route tool access through a broker that can enforce least privilege and tool-specific policies.
3. Evaluate policy before consequential agent steps execute.
4. Require approval when risk, uncertainty, novelty, or sensitivity crosses threshold.
5. Preserve structured traces for review, dispute, learning, and rollback.