AI 造东西，从打草稿到交成品
我们习惯了 AI 生成一段文字、一张图，但交付一份能用的东西，是另一回事。这篇综述把焦点从生成搬到「完成」:AI 干活时，手里的活计会随着中间结果不断调整方向，而不只是按提示跑一遍。作者翻了 259 项相关研究，发现真正的难点不在模态，而在决策之间缠得有多紧、错了要多久才看得见。拆解能减少局部头疼，但拼接和复查的成本会变高；让 AI 自己当裁判，往往只是重复它自己的盲区。这不是你明天能拿来用的技巧，但它帮你重新划了条线：AI 从「给你个草稿」到「给你个成品」，中间还隔着设计和责任。
📄 原文摘要(英文)
Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increasingly depends on whether those pieces can become complete, dependable deliverables. This survey examines agentic artifact creation, which we define as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work. Functionally, the process links an operational representation of the artifact, a construction policy, and runtime verification whose feedback can redirect later actions. We reviewed 259 works available through August 20, 2026: 230 systems meeting this definition and 29 benchmarks of agentic artifact construction. We compare six artifact families, then analyze application settings and evaluation practice as separate dimensions. Across families, construction challenges reflect not only modality but also how tightly decisions are coupled and whether failures become visible while they remain repairable. Decomposition can reduce local complexity while increasing coordination and reassembly costs. Learned judges may add little independent evidence when they share the generator's preferences or blind spots. We formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted repair, and revalidating affected state after change. We also identify opportunities for sustaining coherent, accountable control as artifacts, creator intent, and construction systems evolve. A curated paper list is available at https://github.com/GeminiLight/awesome-agentic-artifact-creation.