Agent Seer: Synthesizing Scenarios from Specification Understanding
To evaluate AI agents using external tools, it is necessary to create real testing scenarios that reflect the combination of practitioners’ tools and multi-round dialogue iterations. Manually creating such scenarios relies on deep domain knowledge, is difficult to expand across tool ecosystems, and the resulting static benchmarks cannot track evolving APIs. The study found that tool specifications (including function names, natural language descriptions, and typed parameter patterns) encode sufficient semantic information to synthesize real evaluation scenarios without manual filtering or real-time tool execution. Agent Seer utilizes this finding to integrate scenarios by understanding the specifications.