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Agent Seer: Synthesizing Scenarios from Specification Understanding — arXiv:2608.26133

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26133
License (as archived): CC0-1.0
Archived: 2026-10-17T15:18:11.837Z
SHA-256 of the archived text: 5b782d8cfdf770860b234e35292cabf5d59f6e8a7a6a23f518136c5ba26b4d7b
Stable URL of this page: https://metraipolis.com/documento/dc_39eebfb032fd

How to cite

Agent Seer: Synthesizing Scenarios from Specification Understanding — arXiv:2608.26133 — arXiv (en), https://arxiv.org/abs/2608.26133 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_39eebfb032fd (Metraipolis archive dc_39eebfb032fd, sha256 5b782d8cfdf770860b234e35292cabf5d59f6e8a7a6a23f518136c5ba26b4d7b)

BibTeX

@misc{dc39eebfb032fd,
  title = {Agent Seer: Synthesizing Scenarios from Specification Understanding — arXiv:2608.26133},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26133},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_39eebfb032fd; sha256 5b782d8cfdf770860b234e35292cabf5d59f6e8a7a6a23f518136c5ba26b4d7b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26133. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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, with no examples, no live tool access, and no domain-specific tuning. This pipeline enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues that exhibit strong tool-calling correctness and conversational coherence. Evaluation quality is measured by applying this pipeline on seven MCP specifications spanning diverse domains and tool-suite sizes and measuring the tool-calling correctness and conversational coherence. The pipeline achieves strong quality across all domains, with complete tool coverage on small and medium specifications. Two findings emerge within this analysis: parameter schema complexity is the strongest correlate of quality variation -- tool-suite size plays a smaller, orthogonal role -- and argument value accuracy is the dominant failure mode among imperfect scenarios, a sub-dimension invisible to coarse-grained name-match metrics.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2608.26133 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T15:15:18.027Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).