Organizations deploying production AI agents have no independent mechanism to verify what agents actually did — audit trails are generated by the same agents being audited, creating an unresolvable self-reporting problem. Existing observability tooling captures logs but not ground-truth verification of agent behavior, leaving governance frameworks structurally hollow. At platform scale this means no third party can attest to agent compliance, liability, or incident reconstruction.

Agent audit trails today are self-reported by the same agent being audited, making compliance attestation, liability assignment, and incident reconstruction structurally untrustworthy.

Engineering and compliance leads at enterprises running production AI agents in regulated or high-stakes domains (fintech, healthtech, legal ops, procurement).

Enterprises already pay $50K-500K/yr for SOC 2 audits, penetration testing, and compliance tooling — they understand paying a third party to independently verify system behavior, and regulators will increasingly demand it for autonomous agents.

MVP is a lightweight sidecar/proxy that intercepts agent tool calls, API requests, and state changes at runtime, hashes and logs them to an append-only ledger independent of the agent's own logging, and offers a diff-view comparing agent-reported actions vs. observed actions; start with LangChain/CrewAI integrations.

Enterprise observability and compliance is $30B+ today; the agent-specific audit layer is a new wedge that grows linearly with agent deployment — conservatively $2-5B within 3 years as agentic AI hits regulated industries.