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. We 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, are AI agents effectively delivering users improvised prototypes rather than systems fit for high-stakes scenarios in which users may unwittingly apply them? This paper argues for the need to integrate rigorous SE processes into the agentic loop to produce production-grade, hardened, and deterministically-constrained agent *workflows* that substantially outperform the potentially brittle and vulnerable results of on-the-fly synthesis. Doing so may require extra compute and time, and if so, we must amortize the cost of rigor through reuse across a broad user community. We envision an *AI Workflow Store* that consists of hardened and reusable workflows that agents can invoke with far greater reliability and security than improvised tool chains. We outline the research challenges of this vision, which stem from a broader flexibility-robustness tension that we argue requires moving beyond the ``on-the-fly'' paradigm to navigate effectively.

翻译：当前AI代理的主导范式是一种“即时生成”循环，即代理在用户提示响应的数秒至数分钟内综合规划并执行动作。我们认为，这种范式绕过了经过严格软件工程（SE）流程——包括迭代设计、严谨测试、对抗性评估、分阶段部署等——正是这些流程为我们带来了当今相对可靠安全的系统。当AI代理专注于快速实时综合时，是否实质上向用户交付的是即兴原型，而非适用于用户可能无意中使用的高风险场景的系统？本文主张将严格的软件工程流程整合到代理循环中，以生成生产级、强化且具有确定性约束的代理*工作流*，其性能显著优于即时生成模式下可能存在的脆弱易错结果。若需为此付出额外算力和时间成本，则必须通过跨用户社区的复用摊销这种严谨性成本。我们设想一种*AI工作流商店*，其中包含经强化的可复用工作流，代理调用时可获得远超即兴工具链的可靠性与安全性。本文概述了该愿景面临的研究挑战，这些挑战源于我们提出的广义灵活性-鲁棒性张力，并认为要有效应对这种张力，必须超越“即时生成”范式。