问HN：你们对代理编码（Agentic Coding）的体验如何？
我最近在更深入地实验代理编码，这让我重新思考了构建软件的方法。
我注意到的一个关键区别是前期成本。在代理编码中，我感受到更高的前期成本：在模型开始生成代码之前，我必须考虑架构、约束和成功标准。我需要将通常保存在脑海中的思维模型外化，以便人工智能能够利用它。
而在“精确编码”中，前期成本几乎是微不足道的，但这仅仅是因为我将大部分复杂性都保留在脑海中。在我编写代码时，所有的设计决策、边界情况和上下文假设都存在于我的脑海中。测试更多的是一个最终验证的步骤。
我意识到，代理编码将我的认知负担从按需执行转变为更具计划性的执行（我表现得更像一个研究者而不是黑客）。我的角色不再是“精确”实现每一段逻辑，而是更清晰地定义问题空间，以便代理能够可靠地组装解决方案。
另一个观察是，由于将代码编写的任务委托给代理，写代码的成本几乎可以忽略不计，因此我需要转变角色，承担起质量保证的职责，以评估代理的输出。
很想听听你的想法！
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I have been experimenting more deeply with agentic coding, and it’s made me rethink how I approach building software.<p>One key difference I have noticed is the upfront cost. With agentic coding, I felt a higher upfront cost: I have to think architecture, constraints, and success criteria before the model even starts generating code. I have to externalize the mental model I normally keep in my head so the AI can operate with it.<p>In “precision coding,” that upfront cost is minimal but only because I carry most of the complexity mentally. All the design decisions, edge cases, and contextual assumptions live in my head as I write. Tests become more of a final validation step.<p>What I have realized is that agentic coding shifts my cognitive load from on-demand execution to more pre-planned execution (I am behaving more like a researcher than a hacker). My role is less about 'precisely' implementing every piece of logic and more about defining the problem space clearly enough that the agent can assemble the solution reliably.<p>Another observation has been that since the cost of writing code is minimal as agents are delegated to write them, there is a need for me to shift and context and also take up the QA role to evaluate the agents output.<p>Would love to hear your thoughts?