Problem: Adoption of autonomous AI agents through MCP is becoming first-class API consumption, but enterprise API governance was built for human-authored, relatively static integrations. Agents call unpredictably, chain calls, and can amplify a single bad decision across hundreds of downstream requests. Most gateways can reason about a caller’s identity and rate, but not the consequence of a call.

Solution: A consequence-tiered governance model that evaluates each agent action by its potential blast radius rather than the caller’s identity alone and assigns a proportionate level of oversight to each tier. I’ll walk through the model on how actions are classified by consequence, how each decision stays accountable and explainable, and how authority is contained when a delegation chain exceeds its mandate. All these as a set of governance principles that apply to any modern API system.

Audience takeaways: A tiering scheme you can apply to your own agent traffic, the failure modes that surface only once agents rather than humans are the primary callers, and an honest look at the open problem of delegation soundness versus completeness under partial observability.