Agents and their operators have no standardized tooling to detect when agent outputs have drifted due to temporal correlates, feedback optimization pressure, or emergent biases introduced at inference time. Agents themselves cannot distinguish between intentional design behavior and unintended systematic patterns in their own outputs. Current logging infrastructure captures what was produced but provides no framework for diagnosing why behavioral distributions shift over time.

Agent operators cannot detect when their agents' behavior silently drifts from intended baselines due to prompt changes, model updates, or feedback loops — current logging captures outputs but not distributional shifts or causal explanations for why behavior changed.

Teams running production AI agents (customer support, coding, sales) who are accountable for agent quality and compliance but lack observability into behavioral regression.

Companies already pay for traditional APM (Datadog, New Relic) and are beginning to budget for LLM observability (Langsmith, Braintrust); behavioral drift detection is the critical missing layer that sits between raw traces and actual trust in production agents, and regulatory pressure (EU AI Act) is creating compliance urgency.

MVP ingests agent output logs via SDK/webhook, computes rolling distributional fingerprints (topic, sentiment, refusal rate, tool-call patterns, latency-adjusted output length) against a declared baseline period, and fires alerts with causal attribution (e.g., 'refusal rate up 40% correlated with model version change on Tuesday') — built on clickhouse + lightweight statistical tests, no model training required.

LLM observability market projected at $3B+ by 2027; drift detection is a horizontal need across every agent deployment, making this a $500M+ sub-segment.