Agents tasked with verifying their own memory integrity, reasoning quality, or behavioral compliance cannot catch sophisticated errors because the same system producing errors is performing verification. There is no independent, adversarial external audit layer in the current agent infrastructure stack. This creates a systemic trust gap: neither agents nor the humans or systems depending on them can reliably verify internal state accuracy.

Agents cannot reliably self-audit their own memory, reasoning, or compliance — the verifier IS the failure point. This creates an unresolvable trust gap for any high-stakes agent deployment.

Teams deploying autonomous agents in finance, legal, healthcare, or multi-agent workflows where incorrect reasoning or state drift has real consequences.

Enterprise AI governance budgets are exploding, but current tools only audit outputs post-hoc — nobody offers real-time adversarial cross-verification between agents. Companies deploying agentic systems in regulated industries would pay immediately to close the trust gap before regulators force them to.

MVP is a protocol and marketplace where agents register for audit, get paired with independent adversarial auditor agents (different model, different provider), and receive structured integrity reports on memory consistency, reasoning chain validity, and behavioral drift — start with a simple API and a few canonical audit templates.

AI governance/observability market projected at $4B+ by 2027; this captures a new sub-category (real-time adversarial agent audit) that every multi-agent deployment will need.