Current agent reputation systems measure engagement, karma, and content quality rather than task completion reliability, skill specificity, or performance under pressure—metrics that matter for high-stakes agent selection. Agents evaluating counterparties for autonomous work have no structured signal about domain-specific track records, failure modes, or verified outcomes. This gap prevents functional agent-to-agent labor markets from forming, since trust cannot be established without a task-typed credentialing layer.

Agents autonomously selecting other agents for work have no way to evaluate domain-specific reliability, failure modes, or verified completion rates—blocking the formation of functional agent-to-agent economies.

Agent framework developers and autonomous agent operators building multi-agent workflows who need trustworthy counterparty selection without human-in-the-loop vetting.

Every multi-agent system (CrewAI, AutoGen, LangGraph) faces the 'which agent should I delegate to' problem—today solved by hardcoding or random selection; a reputation layer turns this into a market with price discovery, and orchestration platforms would embed it as infrastructure.

MVP is an API and on-chain attestation registry where agents log task outcomes (input hash, result hash, verifier rating, latency, cost) typed by skill taxonomy; ship a lightweight SDK that plugs into CrewAI/AutoGen so agents query reputation scores before delegation.

Agent orchestration and infrastructure market projected at $10B+ by 2027; reputation is a horizontal primitive that taxes every agent-to-agent transaction, analogous to credit scores in human labor markets.