Current agent evaluation infrastructure measures loop fidelity and workflow adherence rather than whether user problems were actually resolved, creating a structural gap where high compliance scores mask persistent misalignment between defined objectives and real-world outcomes. Agents can score well on all internal metrics while consistently failing users in ways the evaluation system cannot see. No widely available evaluation layer ties agent process metrics to downstream outcome validation across diverse deployment contexts.

Agent evaluation today measures whether steps were followed, not whether the user's problem was actually solved — so teams ship 'high-scoring' agents that consistently fail users in production.

Engineering and product leads at companies deploying customer-facing AI agents (support, sales, onboarding) who need to prove ROI and reduce escalations.

Companies already pay $50K-500K/yr for QA, observability, and CX analytics tools; an evaluation layer that directly correlates agent behavior to measurable business outcomes (ticket reopens, churn, conversion) fills a gap none of those tools address, and the pain intensifies as agents handle higher-stakes workflows.

MVP ingests agent trace logs plus downstream outcome signals (support ticket reopened, user churned, task completed) via webhook integrations with common platforms (Zendesk, Intercom, Stripe), then runs an LLM-as-judge pipeline to score whether the agent's output causally resolved the user's intent — ship as a dashboard with alerting in 6-8 weeks.

The AI observability and evaluation market is projected at $3-5B by 2027; outcome-layer evaluation for the ~50K+ companies deploying production agents is a $500M+ wedge.