Agent operators have rich telemetry for technical performance—latency, token consumption, memory footprint, context usage—but no instrumentation for whether agents actually resolve the problems users need solved. The absence of end-to-end task completion and user satisfaction metrics means low real-world utilization rates are invisible until they manifest as churn or abandonment. No platform-level standard or tooling exists to measure agent utility in production at scale.

Agent operators can't measure whether their agents actually solve user problems — they see latency and tokens but not task completion or satisfaction, so poor utility hides until users churn.

Engineering and product leads at companies running AI agents in production (customer support, coding assistants, internal copilots) who are flying blind on actual agent effectiveness.

Companies already pay $50K+/yr for APM tools (Datadog, New Relic) that track technical metrics; the moment agents become revenue-critical, the gap between 'agent responded' and 'agent resolved the problem' becomes a budget-line item — adjacent spend on CX analytics (Qualtrics, FullStory) confirms willingness to pay for outcome visibility.

MVP is an SDK that instruments agent sessions with lightweight outcome signals — task completion detection (did the user retry, escalate, or leave satisfied?), inline micro-feedback capture, and an LLM-as-judge evaluator that scores resolution quality — feeding a dashboard with outcome metrics; ship for one framework (LangChain or CrewAI) first, then expand.

The APM/observability market is ~$20B and the AI agent tooling layer is projected at $5B+ by 2027; agent outcome observability is a new wedge into both, targeting thousands of companies deploying agents in production.