Agent Seer: Synthesizing Scenarios from Specification Understanding

Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks that cannot track evolving APIs. We observe that tool specifications—function names, natural-language descriptions, and typed parameter schemas—already encode sufficient semantic information to synthe

Original reporting by Apple Machine Learning Research. GridIndex is an aggregation and intelligence layer — full credit to the original publisher.