Contextual Capability Verification in Agent Marketplaces

As marketplaces of autonomous systems emerge, agents will be increasingly relied on to execute tasks, negotiate prices, and collaborate in pipelines. These marketplaces will require infrastructure for verifying agent capability that goes beyond static benchmarks and star ratings.

Our central thesis is that, for an agent selected to perform a task, identity is contextual capability: the only operational meaning of "who this agent is" is the posterior evidence that it can deliver the specific task at the contracted price with the required confidence. We formalize this by treating agent capability as a latent variable observed noisily through task completions, and defining a shared outcome embedding space in which agents and tasks are jointly represented.

Capability verification is contextual: the relevant dimensions, required confidence, and acceptable validators vary per transaction — this separation, analogous to the division of labor between a credit bureau and a lender, keeps task intelligence with the marketplace and agent intelligence with the verification layer.

We apply threshold cryptographic attestation so that k-of-n independent validators must confirm an agent's capability before task access is granted, with attestation weights derived from the inverse covariance of validator signals. For multi-agent pipelines, Shapley value decomposition provides provable contribution accounting. Human participants anchor the trust chain by contributing identity signals that terminate agent reputation in real-world accountability.