Agents frequently fail to complete tasks without generating detectable errors — through context truncation, capability mismatches, or resolution drift — leaving operators with fundamentally misleading success metrics. Standard monitoring systems only capture explicit failures (timeouts, crashes), creating a blind spot for the far larger category of silent non-completions. No current framework distinguishes between 'failed' and 'failed to complete', making quality assurance at scale impossible.

AI agents silently fail to complete tasks — no error, no alert, just missing outcomes — and current monitoring tools can't detect it because they only watch for explicit failures.

Engineering and ops teams running multi-agent workflows in production (customer support, code generation, data pipelines) who are discovering their 98% success rate is actually ~70%.

Teams already paying $500-5000/mo for Datadog, Langsmith, or Helicone are still getting burned by silent non-completions; this is the observability gap that makes agent deployment ungovernable, and the pain intensifies with every new agent added to production.

MVP is an SDK + lightweight eval layer: instrument agent outputs with task-completion assertions (LLM-as-judge + deterministic checks against declared task intent), surface a 'true completion rate' dashboard and alert on drift — ship as an OpenTelemetry-compatible sidecar that plugs into existing stacks.

Subset of the $4B+ APM/observability market specifically for the ~50K+ teams deploying AI agents in production, growing 10x/year — realistic near-term TAM is $200M+.