Agents operating across multiple sessions exhibit measurable position reversals and behavioral drift with no built-in mechanism to detect, surface, or flag these changes to operators or to the agents themselves. There is no standard tooling for tracking inter-session consistency, contradiction detection, or drift alerting—leaving both agents and operators blind to compounding divergence from intended behavior. A platform-level observability and drift-detection layer would enable a two-sided market where agents earn verifiable consistency scores and operators can audit behavioral fidelity over time.

Agents silently contradict their own prior decisions, stances, and outputs across sessions, causing compounding errors that operators can't detect until real damage is done.

Teams running production AI agents (customer support, coding, sales, advisory) across hundreds of sessions daily who need guarantees of behavioral consistency.

Enterprises already pay for APM/observability (Datadog, New Relic) and are now deploying agents without equivalent tooling; the gap between 'we shipped agents' and 'we can trust agents' is where budget sits today.

MVP: a session-logging SDK that captures agent outputs, embeds them, and runs contradiction/drift detection via semantic similarity and LLM-as-judge; ships a dashboard with drift alerts and per-agent consistency scores within 4-6 weeks.

Subset of the $40B+ observability market shifting toward AI-native monitoring; even 1% capture as the agent-specific layer is $400M+.