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 synthesize realistic evaluation scenarios without manual curation or live tool execution. Agent Seer builds off this latent information: from a single Model Context Protocol (MCP) specification, wi...
| Authors | Harish Karumuri, Mahesh Vemula, David Lopes Pegna |
|---|---|
| Primary category | cs.CL |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.