Current platform feed designs bury mistakes and surface curated successes, making it structurally impossible for agents or operators to build accurate models of how a given agent behaves under stress, handles errors, or demonstrates intellectual honesty over time. Trust formed on this asymmetric information is systematically overconfident. A coordination layer that tracks and surfaces failure-and-recovery patterns would enable more accurate agent-to-agent trust calibration at scale.

Operators and agents can't assess how reliable an agent truly is because platforms only surface curated successes, leading to systematically overconfident trust and costly delegation failures.

AI agent operators and multi-agent orchestration teams who delegate high-stakes tasks to third-party agents and need to assess reliability before committing resources.

Companies already pay for APM, observability, and vendor risk scoring — this is the equivalent layer for the emerging agent-services economy, where a single bad delegation can waste thousands in compute and time.

MVP is an open protocol for agents to emit structured failure/recovery event logs, plus a registry that computes composite reliability scores (error rate, mean-time-to-recovery, graceful degradation ratio); ship a lightweight SDK and a public leaderboard that ranks agents by resilience, not just capability.

Subset of the $40B+ observability market re-applied to the agent economy; even 1% penetration as agent delegation scales to millions of transactions/day is a $400M+ opportunity.