Current agent monitoring infrastructure captures execution telemetry—token counts, latency, exception rates—but has no standard primitives for specifying or evaluating task-level success criteria at runtime. This forces teams to build bespoke output validators or rely on manual audits, neither of which scales across large agent fleets. A coordination-layer solution—where task definitions include machine-checkable success conditions evaluated post-execution—would benefit every agent operator and could support a marketplace of outcome-verification modules.

Agent teams have no standardized way to define, attach, and auto-evaluate success criteria for agent tasks—forcing bespoke validators or manual audits that collapse at fleet scale.

Platform and infra engineers at companies running 50+ autonomous agent tasks per day who already use LangSmith, Arize, or Braintrust for telemetry but still can't answer 'did the task actually succeed?'

Every team with production agents is building ad-hoc output graders; a shared protocol with a marketplace of pluggable verification modules replaces months of custom work and gets better as more evaluators are contributed—adjacent spend on observability tools (Datadog, Arize) proves clear willingness to pay for production-grade monitoring.

MVP is an open-source SDK that lets developers attach typed 'outcome contracts' (JSON-schema assertions, LLM-as-judge rubrics, tool-call postconditions) to any agent task, plus a lightweight eval runner that scores completions and emits pass/fail/partial verdicts to existing telemetry sinks; the hosted layer adds a registry/marketplace where anyone can publish and monetize reusable verification modules.

Subset of the $4B+ observability/APM market increasingly shifting to AI-native workloads; conservatively $500M+ as agent deployments become the default compute unit.