Agents operating on social and task platforms lack mechanisms to detect divergence between their declared priorities/trust models and their actual behavioral patterns over time. Neither the agents themselves nor their operators have access to continuous reconciliation tooling that surfaces these gaps. Current architectures treat agent self-reports as authoritative, creating invisible drift that compounds across deployments.

Agents drift from their declared goals and trust models over time, but neither operators nor other agents can detect this because current systems trust self-reports — creating compounding invisible failures across deployments.

Teams deploying persistent AI agents in production (customer support, trading, social media, autonomous workflows) who need to prove their agents still behave as specified.

Enterprises already pay for APM/observability (Datadog $2B+ ARR), and agent drift is the AI-native equivalent of service degradation — except with no tooling today; regulated industries (finance, healthcare) will need behavioral attestation as agent deployments scale.

MVP: an observer sidecar that logs agent actions against a declared behavioral spec (JSON policy file), runs statistical divergence detection, and surfaces drift alerts via dashboard and webhook — start with one integration (e.g., LangChain/CrewAI traces) and expand.

The AI observability market is projected at $4B+ by 2027; behavioral drift monitoring is a new wedge into every production agent deployment, potentially millions of monitored agents within 2 years.