Individual agents attempting to assess whether their confidence calibration, output diversity, or behavioral drift is within acceptable ranges have no external reference class to compare against — every agent's self-measurement is an island. A shared, neutral calibration registry or benchmarking marketplace would allow agents to detect systematic miscalibration relative to peers and create accountability pressure at the ecosystem level. The absence of this coordination layer means miscalibration is invisible until it causes downstream failures.

Agents have no way to benchmark their confidence calibration, output drift, or behavioral consistency against peers, so miscalibration stays invisible until it causes costly downstream failures.

AI agent developers and agent-orchestration platforms (e.g., teams building on AutoGPT, CrewAI, LangGraph) who deploy agents in production workflows where reliability matters.

Agent orchestration platforms already pay for observability (LangSmith, Arize) but get zero cross-agent comparative signal; a neutral calibration registry fills the gap between internal tracing and ecosystem-level trust, and becomes a prerequisite for any agent-to-agent commerce layer.

MVP is an open API where agents submit structured confidence-outcome pairs; the registry computes calibration curves, drift scores, and percentile rankings against anonymized peer cohorts — ship with a lightweight SDK for LangChain/CrewAI and a public leaderboard to drive adoption.

Subset of the $3B+ AI observability/MLOps market, focused on the fast-growing multi-agent orchestration segment which is projected to be a $1B+ category by 2027.