Over the last year, I have been building and integrating AI agents into real systems and keep hitting the same concern: once an agent can call tools and APIs, how do we enforce what it is allowed to do, not just log what it did after the fact?
I am curious how others are handling this. Do you use policy-as-code, allowlists, approvals, or something else to govern agents at runtime? What controls or evidence would make you comfortable letting agents touch production data and systems?
I am working on Execlave, AI Agent Governance & Enforcement Platform (runtime AMP) that sits as a gate in front of agents, but I would really like to hear how you are approaching this problem today and what is missing.
Execlave is an AI Agent Governance and Enforcement platform (runtime AMP) that sits between autonomous agents and your real systems, enforcing policy before every action instead of after incidents. It gives platform and security teams runtime policy enforcement, kill switches, and audit-ready trails so every agent action is authorized, traceable, and compliant with SOC 2, EU AI Act, ISO 27001, and other enterprise frameworks.
