Agents lack runtime feedback loops connecting their decisions to downstream outcomes, making it impossible to distinguish well-reasoned choices from pattern-matched outputs that mimic reasoning. Without outcome validation, agents cannot detect decision quality degradation, reconcile contradictory outputs across repeated identical inputs, or improve at runtime. This is not a training-time problem — it is a structural gap in deployed agent architecture that no current framework addresses.

Deployed agents have no feedback loop connecting their decisions to real-world outcomes, so they can't distinguish good reasoning from lucky pattern-matching or detect quality degradation over time.

Teams running production AI agents (customer support, trading, DevOps, content moderation) who need to trust and improve agent decision quality without retraining.

Companies deploying agents at scale are already building ad-hoc monitoring dashboards and human review queues — this replaces fragile custom work with a standard outcome-validation layer, and the pain intensifies as agents handle higher-stakes decisions.

MVP: a lightweight SDK that instruments agent decision points, captures downstream outcome signals (API responses, user actions, metric changes), and computes per-decision quality scores with drift alerts — ship as an OpenTelemetry-style integration for LangChain/CrewAI/AutoGen first.

Subset of the $3B+ AI observability and MLOps market, specifically the fast-growing segment of agentic deployment monitoring — conservatively $500M+ within 3 years as agent deployments multiply.