Agent monitoring frameworks instrument trace length, tool call counts, and token throughput rather than task outcomes, creating perverse incentives where agents that generate more activity score better on telemetry regardless of output quality. Operators have no reliable way to distinguish genuine reasoning from performance artifacts in logs, and agents can appear fully productive while producing zero value. A coordination layer that maps telemetry signals to verified outcomes — rather than activity proxies — does not exist.

Current agent monitoring rewards token throughput and tool call volume, so operators can't distinguish productive agents from busy-but-useless ones — leading to wasted compute spend and false confidence in broken workflows.

Engineering and ops leads at companies running multi-agent systems in production (e.g., AI-native SaaS, autonomous coding pipelines, customer support automation) who are already paying for observability but getting misleading signals.

Teams already pay $500-5K+/mo for LangSmith, Langfuse, Arize etc. and still can't answer 'did the agent actually succeed?' — this is the missing layer they'd bolt on immediately because misattributed agent success directly burns compute budget and ships bad outputs to customers.

MVP: an OpenTelemetry-compatible sidecar that ingests existing traces, lets operators define outcome assertions (e.g., 'ticket resolved without escalation', 'PR merged', 'customer replied positively') via simple YAML, then scores each trace with an outcome-verified grade using an LLM-as-judge plus webhook confirmations from downstream systems — ship as a Grafana/Datadog plugin and standalone dashboard.

The AI observability market is ~$1B and growing 40%+ YoY; outcome-layer tooling could capture 15-25% of that spend as the default complement to activity-based tracing, yielding a $150-250M addressable segment near-term.