Three Tiers of Enforcement for AI Agents: Strong, Bounded, Detectable

This article discusses three levels of enforcement for AI agents to ensure accountability and auditability of their actions in production environments.

Details

The article highlights the need for AI agents running in production to have clear accountability - being able to answer what the agent did and prove it. Most AI frameworks today provide little to no enforcement, leaving agents free to call any tool or take any action without an audit trail. The author proposes three tiers of enforcement to address this: 1. Strong enforcement: The agent's tool calls go through a proxy that checks policy before forwarding the request. The proxy signs both the request and response, creating a bilateral receipt that the agent cannot bypass. 2. Bounded enforcement: The agent calls a gate before taking an action, gets the decision (approve/deny) signed, and then reports the outcome, again creating a signed receipt. 3. Detectable enforcement: Every agent action gets a quantum-safe signature that is hash-chained to the previous one. Tampering with the audit trail becomes detectable. The author recommends using all three tiers, with strong enforcement for high-risk mutations and bounded/detectable for routine operations. This provides a comprehensive system to ensure accountability and auditability of AI agent actions in production.