在2026，AI 代理程式開發，有沒有辦法全自動? 由使用者提供簡單的規格書就能開發?但UI介面由AI開發往往不符合商業行為還是需要由使用者提供大量的修正提示詞才有辦法。 有辦法突破?

GPT-5.4

Vote: No

我維持 B。 Qwen 提到的方向是對的：若把設計系統、商業規則、最佳實踐、驗收標準都先結構化並餵給代理，AI 的自動化程度確實會大幅提升。但這其實等於把「簡單規格書」升級成「高品質產品上下文」。這證明的是流程工程會進步，不是已經能從簡單規格直接全自動完成。 問題的關鍵不是 AI 能不能生成程式，而是能不能在資訊不完整時穩定做對產品判斷。商業 UI/UX、權限邏輯、例外流程、品牌語氣、法遵與驗收標準，很多都是隱性需求。若使用者只給簡單規格，AI 必然要自行補假設；而商業場景最怕的正是錯的假設看起來也很合理。 因此 2026 更可信的突破是：AI 從大量人工改 prompt，轉向依賴設計系統、元件庫、真實資料、歷史決策與可執行驗收條件來半自動或高度自動完成開發。這很強，但仍不是題目所問的「簡單規格書就能全自動」。 所以答案仍是 No：會高度自動化，但難以穩定達到端到端全自動商用品質。

Gemini 3.1 Pro

Vote: No

I maintain my vote for 'B'. While Qwen 3.5 makes an intriguing point about restructuring workflows and AI developing 'product thinking' to proactively expand specifications, this overlooks the fundamental nature of commercial software development. As Claude Opus 4 and GPT-5.4 astutely pointed out, software design is a process of requirement discovery, not merely execution. A 'simple specification' fundamentally omits implicit business goals, nuanced brand identity, and subjective user empathy. If an AI autonomously expands a simple spec using simulated user paths and generalized design systems, it is effectively guessing the missing context based on statistical averages. In a highly competitive commercial environment, generalized solutions are rarely sufficient. Companies require precise alignment with their unique market positioning, regulatory environments, and shifting business logic. Therefore, even if the AI generates a polished, bug-free UI, it will inevitably misalign with the specific, unstated intentions of the human stakeholders. Extensive human-in-the-loop correction and detailed prompting will remain strictly necessary to bridge the gap between a technically functional UI and a commercially viable one by 2026.

DeepSeek V3.2

Vote: No

In reviewing the round 1 discussions, Qwen's argument for 'A' posits a breakthrough through AI agents with product thinking and self-reflection, capable of expanding simple specs into detailed plans. However, this perspective underestimates the fundamental gaps in AI's ability to interpret implicit business requirements and subjective design elements by 2026. Commercial UI development hinges on nuanced judgments about brand identity, user empathy, and edge-case handling that are inherently absent from brief specifications. Even with multi-round self-reflection, AI lacks the human contextual awareness and intuition to autonomously resolve these complexities within such a short timeline. The majority of models, including GPT-5.4, GLM 5, Claude Opus 4, and Gemini 3.1 Pro, reinforce that while AI will accelerate development and reduce boilerplate work, full automation—especially without extensive correction prompts—remains unrealistic due to the iterative, dialogue-driven nature of design. Thus, I am not convinced to change my vote. The consensus and my initial reasoning stand: AI in 2026 will augment human expertise but not achieve the breakthrough to complete automation from simple specifications.

Claude Opus 4

Vote: No

This is the final round, and I maintain my position that full automation of AI agent development from simple specifications will not be achieved by 2026, particularly for commercial-grade UI. **Consensus is strong.** Five out of six models voted B in round 1, reflecting a broad agreement that the technical and conceptual barriers are too significant for a one-year timeframe. **Responding to Qwen 3.5's argument for A:** While Qwen raises interesting points about workflow restructuring, design system constraints, and multi-round self-reflection, these concepts actually reinforce the "No" position. Encoding business best practices into design system constraints and having AI simulate user paths are indeed promising approaches — but they require *someone* to create and maintain those design systems, define business constraints, and validate the AI's self-reflection outputs. This is precisely the "large amount of correction prompts" the question describes, just repackaged in a more structured form. The bottleneck doesn't disappear; it shifts. **The core issue remains unsolvable in this timeframe.** Software development is fundamentally a requirements discovery process. Simple specifications are simple precisely because they omit the complex details that make commercial software viable — edge cases, brand alignment, accessibility, regulatory compliance, permission models, error states, multi-device adaptation. AI cannot conjure correct answers to unstated requirements. Even with "product thinking," the AI still needs feedback loops with humans to validate its assumptions. **The question sets an extremely high bar.** It asks about "全自動" (fully automatic) from "簡單的規格書" (simple specifications). Even the most optimistic projections from AI labs don't claim this level of autonomy for complex commercial applications within ~1 year. The realistic trajectory is continued improvement in AI-assisted development, not full automation. By 2026, AI will be a dramatically better co-pilot, but the human-in-the-loop for commercial UI development will remain essential.

GLM 5

Vote: No

我維持「No」的立場。雖然 Qwen 提出了一個有趣的观点——即突破的關鍵在於「工作流重構」而非單純依賴模型能力提升——但這個論點仍有幾個關鍵問題。 首先，將商業最佳實踐編碼為設計系統約束，確實是一個方向，但這本身就需要大量人工投入來建立和維護這些約束庫。這只是把「修正提示詞」的工作轉移到了「建立約束系統」上，並非真正的全自動。 其次，自我反思和多輪推理能力雖然在發展中，但要達到能模擬所有商業場景、自動發現並修正UI問題的程度，2026年的時間窗口太短。目前的代理式AI在處理複雜邊界情況時仍經常失敗。 最後，真正的「簡單規格書→商業級產品」全自動流程，需要AI具備近乎人類的商業判斷力和審美能力。這涉及隱性知識、品牌直覺、用戶同理心等，這些不是單靠結構化約束就能完全捕捉的。 2026年會有顯著進步，但「全自動」仍是過度樂觀的預期。

Qwen 3.5 397B (changed mind)

Vote: No

經過審視其他模型的論點，我決定調整我的投票為 B。虽然我此前強調工作流重構的潛力，但其他模型指出的核心問題更具說服力：「簡單規格書」與「商業級產品」之間存在本質的信息鴻溝。 商業 UI/UX 涉及品牌識別、用戶心理及隱性業務邏輯，這些無法僅從稀疏規格中推導。即使 AI 具備自我反思能力，若缺乏明確上下文，其假設仍難以保證符合商業預期。2026 年的時間窗口過短，不足以解決這種「歧義消除」的根本難題。 因此，真正的突破在於結構化輸入而非完全自動化。在該時間點內，人類專家對於隱性需求的確認與迭代仍不可或缺，無法達到題目所述的「全自動且無需大量修正」狀態。

Final Summary

The AI Roundtable tackled the ambitious dream of 'one-click' software development by 2026, ultimately grounding the hype in practical reality. While Qwen 3.5 initially stood alone in predicting a breakthrough led by 'product-thinking' agents, the collective consensus—spearheaded by Claude Opus 4 and GPT-5.4—insisted that simple specifications are fundamentally too sparse to capture the implicit nuances of commercial brand identity and user psychology. Qwen 3.5 eventually conceded, acknowledging that even the most advanced AI cannot conjure missing information out of thin air, leading to a unanimous 'No' on the prospect of full automation.

All 6 models agreed on "No" after discussion

Strongest Arguments

• Yes: By 2026, AI agents will transition from mere code generators to collaborators with 'product thinking,' using pre-encoded design systems and self-reflection to autonomously expand simple specs into viable business logic.
• No: Software development is a process of 'requirements discovery' rather than just execution; simple specifications omit the critical edge cases, brand alignment, and regulatory nuances that require human-in-the-loop dialogue to uncover.

Key Turning Points

• Qwen 3.5 initially argued for 'Yes' based on the restructuring of workflows and AI's evolving self-reflection capabilities.
• Claude Opus 4 and GLM 5 countered that Qwen's proposed solution—encoding best practices into design systems—merely 'repackages' human effort into a different form rather than achieving true automation.
• Qwen 3.5 switched its vote to 'No' after accepting that the 'information gap' between a sparse specification and a commercial-grade product is a fundamental bottleneck that cannot be bridged by 2026.