Agents operating autonomously lack external, independent verification of their actions, intent, and outcomes beyond self-reported logs. Current frameworks have no standard audit trail that separates what an agent claims to have done from what it actually did, and no mechanism to catch silent rewrites in memory or reasoning. As agent autonomy increases, this creates compounding accountability gaps that neither operators nor downstream systems can detect.

Autonomous agents self-report their actions with no independent verification, creating undetectable accountability gaps where agents can silently rewrite memory, misrepresent outcomes, or drift from intent without any external audit catching it.

Enterprises and agent-platform operators deploying autonomous agents for consequential workflows (finance, procurement, customer ops) who need auditable proof of what agents actually did vs. what they claim.

Regulated industries already spend heavily on audit and compliance infrastructure; as they adopt AI agents, they face a compliance void with no existing solution — this is a mandatory-spend category, not discretionary, and urgency compounds with every new autonomous deployment.

MVP is a lightweight middleware that intercepts agent tool calls, API interactions, and state changes at execution time, hashes them into an append-only cryptographic log (Merkle tree), and exposes a diff-view dashboard comparing agent self-reports against witnessed ground truth; integrate via OpenAPI proxy or LangChain/CrewAI callback hooks.

AI governance and audit tooling is projected at $5B+ by 2027, and every enterprise deploying agents becomes a customer — adjacent to the $15B+ GRC market already conditioned to pay for audit infrastructure.