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Towards Portable Specification of the Agent Harness

Summary

The paper argues that the harness surrounding a language model has no settled definition, decomposes it into eight functional components, and proposes a portable specification framework recording which model ran in which harness.

SourcearXiv AIAuthor: Nicolas Gertler, Claudio Novelli, Luciano Floridi
Towards Portable Specification of the Agent Harness
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[Submitted on 29 Oct 2026]

Title:Towards Portable Specification of the Agent Harness

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Abstract:Large language models act only through the infrastructure that surrounds them. That infrastructure, the harness, determines what a model can accomplish and how it fails, yet it has no settled definition. To address this gap, we decompose the harness into eight functional components that can be specified and compared across systems, and we propose a portable specification framework that records which model ran in which harness. The record is needed because different harness configurations can yield several-fold differences in reported scores for the same model, and because much of an agent's attack surface lies in its harness. Existing model cards and agent cards provide release and operational information, but do not establish the sequence of effective configurations within a run. Harnesses can change between releases and between runs, and, where agents edit their own tools and configuration, within a run. Enacted AI laws contain documentation duties relevant to covered systems, subject to their respective scopes and application dates, but do not prescribe the common harness record proposed here. Therefore, the harness has to be explicitly described and specified as a versioned part of the system.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.40917 [cs.AI]

(or arXiv:2610.40917v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.40917

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nicolas Gertler [view email] [v1] Thu, 29 Oct 2026 19:43:14 UTC (769 KB)

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Key points

  • The harness is decomposed into eight functional components that can be specified and compared across systems
  • Different harness configurations can produce several-fold differences in reported scores for the same model
  • Harnesses can change between releases, between runs, and even within a run when agents edit their own tools
  • Model cards and agent cards do not capture the sequence of effective configurations within a run, and current AI laws do not mandate such a record

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