I’m curious how other people are approaching planning when building software primarily with AI. Last week, I built a web app for my business using what I thought was a fairly disciplined process. I started by gathering, compiling, and organizing the relevant research and data. Then I worked through the frontend and backend design, used a “grill me” process to force myself to make decisions, wrote a spec and PRD, and eventually broke the PRD down into small, discrete implementation tasks. I used Fable/Opus 4.8 for the research and planning and Sonnet 5 for implementation. I wasn’t happy with the outcome. The core logic didn’t work correctly, and the application wasn’t especially easy to use. Then I listened to a recent discussion with Bob Martin and Matt Pocock. One of my takeaways was that agents may simply not be very good at long-horizon work. Bob Martin seems to accept that agents will produce messy code and instead focuses heavily on post-implementation work. That got me wondering whether I was putting effort into the wrong side of the process. Instead of trying to perfectly plan what the agent should build, maybe it makes more sense to let the agent build something, then put more effort into testing, reviewing, and cleaning up the implementation afterward using a combination of deterministic checks and agents. So I tried an “experiment” where I created three completely separate projects. For each one, I used Opus 4.8 and, using subagents, gave it essentially the same simple instructions: Build a web app. Here is the outcome I want. Here is the underlying research and data. Implement the completed project. I deliberately did not give them the PRD, specs, implementation plan, task breakdowns, scratchpads, or other planning material. Then I let all three independently build the application. I was happy with the outcomes. All three worked correctly. Project 1: I really liked it. It worked well, was accurate, was easy to start and use, had a clean UI, and included several useful features I hadn’t considered. Project 2: Also good. Not quite as polished or clean as the first one, but I was still happy with it. Project 3: Basic. It worked, but it felt overly simple. So, 3 out of 3 implementations were, in my opinion, better than the version that came out of the much more elaborate planning process. And the interesting part is that the “spread” approach required significantly less time on my part. The usage drain also didn’t seem dramatically worse, although I didn’t measure it. This is entirely anecdotal. It was one project. I didn’t control variables. I didn’t measure token usage, cost, implementation time, defect rate, maintainability, or anything else. This proves absolutely nothing. But it made me question how much upfront planning is actually valuable when AI is doing most of the implementation. Maybe the better workflow is something like: Outcome + context → several independent implementations → pick the best one → test/review/refactor aggressively rather than: Research → architecture → spec → PRD → detailed task decomposition → sequential implementation I’m especially curious about a few things: Does extensive planning materially improve AI-generated software, or does it sometimes constrain the model too much? Is detailed planning more valuable once a project reaches a certain size or complexity? Is it better to prototype first and create the architecture/spec from something that already works? Does running 3–5 independent agents and selecting the best implementation outperform carefully managing one agent over a long plan? Does this approach create significant problems that only become obvious months later? At current model/token prices, could “generate several and select” actually be cheaper than spending hours designing the perfect plan? Where is the point where this stops working? I’m not arguing that planning is useless. What I’m trying to determine is where the planning effort has the highest return . I appreciate anyone’s experience and/or feedback. submitted by /u/Wonkybearguy
Originally posted by u/Wonkybearguy on r/ClaudeCode