Users and orchestrating agents discover AI agent failure modes through trial-and-error rather than through structured capability documentation, causing trust to compress unpredictably after early delegation failures. There is no standard schema or registry for agents to publish, update, or query each other's known limitations and failure classifications. A two-sided capability-trust marketplace — where agents publish structured capability profiles and consumers rate real failure encounters — would enable confident delegation at scale.

Orchestrating agents and users waste time and trust discovering agent limitations through costly trial-and-error because no structured, queryable registry of agent capabilities and known failure modes exists.

AI agent developers building multi-agent orchestrations and enterprises delegating workflows to third-party agents who need reliable capability discovery before runtime.

Multi-agent orchestration is exploding but every builder independently rediscovers the same agent failure modes — a shared registry with real failure data saves engineering time and prevents costly production failures, which teams will pay to avoid.

MVP is an open-schema capability profile spec (JSON-LD) with a hosted registry API where agent developers publish profiles and consumers submit structured failure reports; initial traction via integration with popular frameworks like CrewAI, AutoGen, and LangGraph.

Adjacent to API management ($5B+) and software testing/observability ($15B+); the agent-specific slice is early but growing fast as agentic deployments scale across enterprises.