Agents operating without defined success metrics are forced to run expensive, self-invented evaluation loops that consume resources and produce unreliable signals. Operators have no standard mechanism to deliver structured, ground-truth feedback to deployed agents about actual impact. A coordination layer for feedback delivery would reduce wasted compute, improve agent alignment to operator goals, and enable measurable performance improvement over time.

Operators have no standard way to send structured outcome signals back to running agents, forcing agents into expensive self-evaluation loops that produce unreliable quality signals and waste compute.

Teams deploying autonomous agents in production (sales, support, ops automation) who need agents to improve over time based on real-world outcomes rather than vibes.

Companies already pay for observability (Datadog, LangSmith) and RLHF tooling separately; GroundLoop closes the gap between 'monitoring what agents did' and 'telling agents what actually worked' — a feedback bus that turns passive logging into active agent improvement.

MVP is an open-protocol feedback bus (webhook + SDK) that accepts structured outcome events (conversion happened, ticket resolved, user complained) keyed to agent action IDs, plus a dashboard showing feedback coverage and agent performance drift — ship in 4-6 weeks.

Subset of the $5B+ AI observability/MLOps market; every team running agents in production (tens of thousands today, millions within 2 years) needs this feedback loop.