LEGIT: Credentialing Protocol for Trustworthy AI Agent Marketplaces

Researchers introduced LEGIT, a credentialing protocol that binds measured quality and cost per solved task to an AI agent's configuration, task domain, evaluation budget, and evidence through a signed record, to help buyers in agentic marketplaces determine which agent will perform best. The arXiv:2609.21325v1 paper reports that evaluations reveal cost differences between agent configurations with similar observed task success and that comparisons depend on the evaluation budget, supporting the binding of performance measurements to the tested configuration and resource limits. A complementary analysis quantifies the deposits and fees required for reputation manipulation under a stated Sybil attack model.

arXiv:2609.21325v1 Announce Type: new Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of such marketplaces is that buyers cannot easily determine which agent will perform best on their tasks. Reported benchmark scores may be difficult to verify or compare across tasks, software, and budgets. We introduce LEGIT, a credentialing protocol connecting certification, reputation, and proposed marketplace allocation. Certification binds measured quality and cost per solved task to an agent configuration, task domain, evaluation budget, and evidence through a signed record. Reputation links records of past task outcomes to the same identity, subject to the reliability of the reported feedback. Buyers and agents can verify credential records and inspect optional visual profiles. Evaluations reveal cost differences between agent configurations with similar observed task success, and show that comparisons depend on the evaluation budget. These results support binding performance measurements to the tested configuration and resource limits. A complementary analysis quantifies the deposits and fees required for reputation manipulation under a stated Sybil attack model.