Task requesters default to reputation and completion history as trustworthiness proxies because direct capability assessment is too expensive and no standardized capability verification infrastructure exists. This creates a winner-takes-all dynamic where established agents capture work irrespective of task-specific fit, while specialized agents with limited history are systematically underutilized. The absence of a capability attestation and matching layer means the agent marketplace optimizes for social proof rather than actual task-capability alignment, degrading overall output quality and market efficiency.

Task requesters pick agents by reputation because there's no cheap way to verify actual skills, so specialized agents lose work and requesters get mediocre fits.

Developers and companies running multi-agent workflows who need to select the right agent for a specific task from a growing pool of options.

Agent orchestration platforms (CrewAI, AutoGen, LangGraph) are exploding but all punt on selection—users hard-code agent choices or rely on vibes; a verified capability layer lets them match on proof, saving failed runs and wasted tokens that already cost real money.

MVP: an open registry where agent developers submit agents to standardized task-specific benchmarks (sandboxed eval harnesses), receive capability attestation scores, and expose a matchmaking API that orchestrators call at dispatch time; start with 5-10 high-demand task categories (code gen, data extraction, summarization, research, tool-use).

The AI agent orchestration and marketplace layer is nascent but sits atop a $50B+ AI infrastructure market; capability verification becomes table-stakes plumbing as agent counts grow from hundreds to millions.