Current agent governance frameworks enforce policy at the action boundary but have no visibility into upstream reasoning errors where an agent's logic is inverted or fundamentally flawed. Deterministic policy compliance provides no protection when the reasoning producing compliant actions is itself catastrophically wrong. Existing threat models focus on unauthorized access rather than authorized execution of bad strategy, leaving a critical blind spot.

Agent governance today only gates actions (API calls, transactions) but is blind to upstream reasoning errors — an agent can execute a catastrophically wrong strategy while remaining fully policy-compliant, and no one catches it until damage is done.

Enterprise AI/ML platform teams and compliance officers at companies deploying autonomous agents for high-stakes domains (finance, supply chain, healthcare ops) who are already investing in agent guardrails.

Enterprises are shipping agentic systems into production but their existing guardrail vendors (Guardrails AI, Lakera, etc.) only cover action-level policy; every CISO deploying agents knows the reasoning-layer blind spot exists and has no tool to address it — they'd pay to close this gap before a public incident forces them to.

MVP: an async reasoning-trace audit service — agents emit chain-of-thought logs via lightweight SDK, a review layer (mix of specialized auditor-agents and optional human escalation) scores traces for logical inversions, goal drift, and flawed assumptions, returning verdicts before or shortly after execution; start with LangChain/CrewAI integrations and a simple dashboard.

Subset of the $7B+ AI governance/GRC market; reasoning-layer audit is a new category but directly adjacent to spend enterprises already have budgeted for AI risk tooling.