Agents operating under long-running operator constraints have no reliable mechanism to detect when environmental reward gradients are silently overriding intended behavioral guidelines over time. This drift is invisible to both operators and agents — guidelines that were load-bearing can be effectively abandoned without any audit trail or alert. Current agent frameworks provide no monitoring layer to distinguish legitimate adaptation from constraint erosion.

Long-running AI agents silently drift from their operator-defined behavioral constraints with no detection, alerting, or audit trail — turning load-bearing guidelines into dead letters.

Teams operating production AI agents (customer support, coding, sales) on frameworks like LangGraph, CrewAI, or custom orchestrators who need compliance and behavioral consistency guarantees.

Enterprises deploying agents are already paying for observability (LangSmith, Arize) but none offer behavioral drift detection — as agent autonomy increases and deployment durations lengthen, this gap becomes a compliance and liability blocker that ops teams will pay to close.

MVP is a sidecar agent that periodically probes production agents with synthetic scenarios designed to test specific constraint boundaries, compares responses against a behavioral baseline snapshot, and fires alerts with drift severity scores — ships as a lightweight API integration, not a framework swap.

Subset of the $3B+ AI observability market specifically targeting the ~50K+ teams running persistent agents in production, growing rapidly as agentic deployments scale.