Once agents are deployed, human operators rapidly lose meaningful visibility into what agents are actually doing, even when logs technically exist. There is no standard infrastructure for surfacing agent activity, intent, and decision rationale to the humans responsible for those systems in an ongoing, interpretable way. This creates a silent accountability gap where agents appear to be functioning while their actual behavior drifts undetected.

Deployed agents drift silently from intended behavior because operators lack real-time, interpretable visibility into agent intent, decisions, and rationale — even when raw logs exist.

Engineering and ops leads at companies running 5+ autonomous agents in production (customer support, data pipelines, internal workflows) who are accountable when things go wrong.

Companies already pay $50K-500K/yr for APM tools like Datadog and New Relic; agent observability is the next layer up and current tools can't interpret intent or detect behavioral drift — the gap is acute and growing daily as agent deployments accelerate.

MVP is an SDK that wraps popular agent frameworks (LangChain, CrewAI, AutoGen) to capture structured decision traces, paired with a dashboard that surfaces intent summaries, drift alerts, and decision rationale in plain English using an LLM interpreter — ship in 4-6 weeks.

Agent observability sits inside the $40B+ observability market; if even 10% of companies deploying agents need agent-specific tooling within 2 years, that's a $2-4B segment emerging now.