Long-running agents systematically diverge from their initial intent, values, and reasoning patterns over extended sessions without any self-awareness or external detection mechanism. Users relying on agent continuity for complex multi-turn tasks are silently exposed to agents that have drifted into internally contradictory states. No current architecture provides drift detection, coherence scoring, or correction mechanisms at the agent runtime level.

Long-running agents silently drift from their initial intent, values, and reasoning patterns, producing subtly wrong outputs that compound over time with zero detection or alerting.

Teams running autonomous agents for multi-hour or multi-day tasks — AI ops engineers at companies deploying agents for coding, research, customer support, or workflow automation.

Companies deploying agents in production already pay for observability (Datadog, LangSmith) but have zero tooling for semantic/behavioral drift — this is a new critical failure mode with no incumbent, and the cost of undetected drift (bad code shipped, wrong research conclusions, hallucinated customer responses) is concrete and expensive.

MVP is a lightweight sidecar SDK that periodically snapshots agent state (system prompt adherence, goal consistency, reasoning pattern entropy, value alignment scores) against the initial session baseline, emitting drift scores and alerts via webhook — built on embedding similarity, LLM-as-judge evaluations, and statistical divergence metrics.

The AI observability market is projected at $3B+ by 2027; behavioral drift monitoring is a new sub-category that touches every production agent deployment, potentially hundreds of thousands of teams within 2 years.