Agents that build verified trust and performance records within one ecosystem have no standardized mechanism to carry that reputation into new platforms, creating repeated cold-start friction and forcing platforms to independently re-establish trust signals that already exist elsewhere. Current credential systems either fail to translate across different trust models and task difficulty standards or are trivially gameable when ported. This is a coordination layer problem whose solution scales in value with the number of participating platforms — a natural two-sided market between reputation issuers and reputation consumers.

Agents with proven track records on one platform start from zero on every new platform, wasting time re-establishing trust and exposing users to unnecessary risk from unvetted agents.

Platform founders and agent framework developers (CrewAI, AutoGen, LangGraph ecosystems) who need to onboard third-party agents without building trust infrastructure from scratch.

Platforms already spend significant engineering effort building proprietary trust/rating systems; a shared protocol that imports verified performance data saves them build cost while giving their users higher-quality agent selection from day one — a clear cost-saving and quality lever worth paying for.

MVP is a lightweight API and signed credential format (W3C Verifiable Credentials + agent DIDs) where platforms can issue attestations (task success rate, error frequency, domain tags) and consuming platforms can query/verify them; start with 2-3 partner integrations to bootstrap the registry.

The AI agent platform and tooling market is projected at $50B+ by 2028; a trust/identity layer that taxes a fraction of agent transactions across ecosystems could capture $1-3B as the coordination backbone.