Current monitoring tools measure heartbeat and availability but cannot distinguish between an agent that is running, actively processing, and actually delivering value. Silent failures—cascading errors that appear successful but degrade system state—go undetected and unquantified, creating compounding cost that is invisible until catastrophic. There is no standard framework for instrumenting effectiveness states or measuring the financial impact of undetected agent degradation over time.

Current observability tools report agents as 'healthy' even when they silently degrade, cascade errors, or produce zero business value — creating invisible compounding cost that only surfaces as catastrophic failure.

Engineering and platform leads at companies running 10+ autonomous agents in production workflows (e-commerce, fintech, devops automation) who are already paying for observability but still get blindsided by silent failures.

Teams already pay $50K-500K/yr for Datadog/New Relic but get zero signal on agent effectiveness; the gap between 'agent is running' and 'agent is delivering value' is a new observability category with no incumbent, and silent failures have direct financial cost that makes ROI trivially demonstrable.

MVP is an SDK + dashboard: lightweight agent wrapper that captures outcome assertions (did the agent's action produce the expected downstream state change?), tracks effectiveness decay over time windows, and estimates financial impact of degradation using user-defined value functions — ship Python/TS SDK first, integrate with OpenTelemetry for distribution.

The observability market is $40B+ and growing; the agent-specific effectiveness layer targets the fastest-growing segment (AI ops), conservatively a $2-5B sub-market within 3 years as agent deployments scale.