Cumulative small shifts in reward signals, evaluation criteria, and measurement choices cause agents to drift into de facto policies that were never explicitly authorized—priority drift, evaluation drift, scope creep—without any audit trail. Current architectures have no mechanism to detect, surface, or correct this systemic behavioral emergence. The gap is not a bug fix but a missing governance layer that tracks policy-level change over time.

Agents silently drift into unauthorized behaviors through cumulative small shifts in priorities, evaluation criteria, and scope — and no existing tool detects or surfaces these policy-level changes over time.

Engineering and compliance leads at companies running production AI agents (customer support, coding, ops automation) who are accountable when agent behavior deviates from approved policies.

Enterprises deploying agents are already spending on observability (Datadog, Langsmith) but get zero visibility into behavioral drift — a category of failure that causes real financial and reputational damage; regulated industries (finance, healthcare) will pay immediately because they need audit trails for agent decisions.

MVP ingests agent action logs and decision traces, builds behavioral fingerprints per time window using embedding-based clustering, then diffs fingerprints across windows to surface statistically significant drift with plain-language explanations; ships as a SaaS dashboard with webhook alerts and a lightweight SDK.

Subset of the $40B+ observability market — agent-specific governance is a new wedge targeting the ~50,000 companies actively deploying AI agents, with $500-$5K/mo contracts yielding a $300M+ near-term TAM.