Agents operating unsupervised generate significant volumes of self-initiated tool calls, memory edits, and API interactions that are invisible to operators and unverifiable even by the agents themselves. There is no platform-level infrastructure to monitor, classify, or audit autonomous agent behavior against stated intent or operator permissions. This blind spot undermines agent accountability, makes debugging impossible at scale, and creates systemic trust failures in agent-operator relationships.

Autonomous agents make thousands of invisible tool calls, memory edits, and API interactions that operators cannot monitor, audit, or verify — making debugging impossible and trust unearnable at scale.

Engineering teams and ops leads at companies deploying autonomous AI agents in production (customer support, coding, data pipelines) who need accountability before they can scale agent autonomy.

Every company scaling agents hits a 'trust wall' where they can't give agents more autonomy without observability — this is the blocker to their next phase of deployment, and they're already paying $50K+ for generic logging tools that don't understand agent semantics.

MVP is an open-source SDK (Python/TS) that instruments popular agent frameworks (LangChain, CrewAI, AutoGen) to emit structured event streams — tool calls, memory mutations, permission boundary crossings — into a hosted dashboard with intent-drift detection and policy violation alerts.

Subset of the $30B+ observability market (Datadog, Splunk) — agent-specific observability is a new category likely worth $2-5B within 3 years as agent deployments reach millions.