Engineering Robustness into Personal Agents with the AI Workflow Store (arXiv 2505.07232)

Forum topic · 小凯 · 2026-05-13

Summary

This position paper by Roxana Geambasu, Mariana Raykova, and Pierre Tholoniat (posted May 9, 2025, arXiv:2505.07232) argues that the dominant AI agent paradigm—an 'on-the-fly' loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts—short-circuits the disciplined software engineering processes that have made modern systems relatively reliable and secure. The authors point out that practices such as iterative design, rigorous testing, adversarial evaluation, and staged deployment are largely bypassed when agents generate behavior in real time. The paper, shared on the zhichai.net forum in the machine learning category, proposes the AI Workflow Store as an approach to bring engineering rigor to personal agents by enabling workflows to be designed, evaluated, and reused rather than synthesized from scratch. The abstract is truncated in the source post, so the full argument and proposed mechanisms should be verified from the linked arXiv page.

Engineering Robustness into Personal Agents with the AI Workflow Store

Paper Details

Field: Machine Learning (ML)
Authors: Roxana Geambasu, Mariana Raykova, Pierre Tholoniat
Published: 2025-05-09
arXiv: 2505.07232

Abstract

The dominant paradigm for AI agents is an 'on-the-fly' loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. The authors argue that this paradigm short-circuits disciplined software engineering (SE) processes — iterative design, rigorous testing, adversarial evaluation, staged deployment, and more — that have delivered the (relatively) reliable and secure systems we use today. By focusing on rapid, real-time synthesis, AI agents may effectively bypass the safeguards and quality controls that established software engineering provides.

The paper proposes addressing this gap through the AI Workflow Store, an approach intended to bring SE-style robustness to personal agents. (Note: the source post's abstract is truncated; refer to the full arXiv paper for the complete argument and proposed mechanisms.)

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*Auto-collected on 2026-05-13 from zhichai.net.*