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This paper proposes a programming languages agenda to enhance the reliability of AI agents by treating them as structured, inspectable software artifacts rather than unstructured probabilistic systems. It argues for formalizing agent behavior through traces and state specifications to enable rigorous testing, monitoring, and repair before deployment. The approach aims to provide sufficient structure for reasoning about agent actions without forcing them into deterministic models.
Sources coveringarXiv, LessWrong, Quantum Frontiers, Latent Space
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Agents as Software: A Programming Languages Agenda for Agent Reliability

arXiv2 min read

arXiv:2609.32198v1 Announce Type: cross Abstract: AI agents increasingly resemble software systems: they call tools, remember facts, follow policies, delegate work, and take actions with real consequences. % Yet the ``program'' of an agent is scattered across prompts, tools, memories, workflows, and execution traces, making its behavior difficult to inspect through ordinary testing and debugging alone. % This essay argues that a programming-systems perspective offers a natural lens for making agents reliable. % We recast agents as programmable artifacts whose behavior can be specified over traces and state, checked before deployment, monitored during execution, and improved from observed failures. % The goal is not to make probabilistic agents behave like deterministic programs, but to give them enough structure that their behavior can be reasoned about, controlled, and repaired.

Abstract:AI agents increasingly resemble software systems: they call tools, remember facts, follow policies, delegate work, and take actions with real consequences. % Yet the ``program'' of an agent is scattered across prompts, tools, memories, workflows, and execution traces, making its behavior difficult to inspect through ordinary testing and debugging alone. % This essay argues that a programming-systems perspective offers a natural lens for making agents reliable. % We recast agents as programmable artifacts whose behavior can be specified over traces and state, checked before deployment, monitored during execution, and improved from observed failures. % The goal is not to make probabilistic agents behave like deterministic programs, but to give them enough structure that their behavior can be reasoned about, controlled, and repaired.

Accepted to Onward! at SPLASH 2026

Programming Languages (cs.PL); Artificial Intelligence (cs.AI)

arXiv:2609.32198 [cs.PL]

(or arXiv:2609.32198v1 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2609.32198 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 225 words

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