[{"data":1,"prerenderedAt":117},["ShallowReactive",2],{"blog-forest-for-the-trees":3},{"id":4,"title":5,"body":6,"date":108,"description":109,"extension":110,"meta":111,"navigation":112,"path":113,"seo":114,"stem":115,"__hash__":116},"blog/blog/forest-for-the-trees.md","Seeing the Forest for the Trees: Why AI-Generated Code Needs Better Task Decomposition",{"type":7,"value":8,"toc":97},"minimark",[9,13,20,23,28,31,34,38,41,44,48,51,54,58,61,64,68,71,74,77,81,84,87,91,94],[10,11,12],"p",{},"AI can generate code faster than ever before, but there's a problem: it's terrible at understanding the bigger picture.",[10,14,15],{},[16,17],"img",{"alt":18,"src":19},"A dense forest viewed from above with one tree highlighted, representing the challenge of maintaining big-picture awareness while working on individual tasks","/blog/forest-for-the-trees.png",[10,21,22],{},"I've spent over 25 years building software in remote and distributed environments, and one thing has become crystal clear in the age of AI-assisted development: speed without structure creates chaos. We're in this fascinating moment where AI can churn out code at incredible velocity, allowing us to deliver features faster than we ever thought possible. But this acceleration has exposed a fundamental flaw in how AI approaches complex software development tasks. The problem isn't the quality of the code it generates—it's how AI fundamentally misunderstands the principles that human engineers have spent decades refining about breaking down work into manageable, deployable units.",[24,25,27],"h2",{"id":26},"the-pull-request-problem","The Pull Request Problem",[10,29,30],{},"Over the years, the software industry has collectively moved toward a practice of smaller, more frequent pull requests. This isn't just a stylistic preference—it's a risk mitigation strategy that has proven itself time and again. When you deploy smaller changes, you reduce the blast radius of any potential issues. You make code review actually feasible for your teammates. You enable continuous iteration toward a larger feature without betting everything on one massive deployment. Most experienced engineers intuitively understand that there's a threshold—maybe 10 files, maybe 20, maybe a certain number of lines changed—beyond which a pull request becomes genuinely difficult to review effectively.",[10,32,33],{},"Here's what happens when you hand AI a large, complex task: it produces one massive pull request. The code might be perfectly functional, it might even be well-written, but it's overwhelming for human reviewers. You're staring at hundreds of lines of changes across dozens of files, trying to hold the entire context in your head while verifying that everything works together correctly. This defeats the entire purpose of code review and reintroduces all the risks that smaller pull requests were designed to eliminate.",[24,35,37],{"id":36},"the-failed-solution","The Failed Solution",[10,39,40],{},"The obvious fix seems simple: just ask the AI to break up that massive pull request into smaller ones. I've tried this approach over and over again, and it fails every single time in the same predictable way. The AI will dutifully split your giant PR into smaller chunks, but those chunks end up being tightly coupled in ways that completely undermine their independence. One pull request will reference code that doesn't exist yet because it's supposed to be added in a later PR. Another will include comments about functionality that hasn't been implemented. The pieces become a sequential chain where each link depends entirely on the previous one, making them impossible to deploy independently.",[10,42,43],{},"This coupling problem reveals something important about how AI approaches problem-solving versus how experienced engineers actually work. When you ask AI to split an already-completed solution, it's essentially trying to reverse-engineer separation points in code that was conceived as a monolithic whole. The dependencies are already baked in. The architectural decisions have already been made. You can't retrofit true independence into a solution that wasn't designed for it from the start.",[24,45,47],{"id":46},"how-engineers-actually-break-down-work","How Engineers Actually Break Down Work",[10,49,50],{},"When I'm working on a complex feature myself, I don't build the entire thing and then figure out how to slice it up. Instead, I start with decomposition at the planning stage. I look at the overall objective—let's call it an epic—and I break it down into smaller sub-tasks before writing any code. Each of those sub-tasks is designed from the beginning to be independently valuable and independently deployable. This isn't just about making smaller pull requests; it's about thinking through the architecture and implementation sequence in a way that allows for iterative delivery.",[10,52,53],{},"This is the fundamental insight that AI struggles with: the solution to massive, unreviewed pull requests isn't breaking them up after the fact—it's breaking down the work into smaller work at the very beginning. We do this intuitively as engineers because we understand both the technical dependencies and the value delivery sequence. We know which pieces need to exist first to support later pieces. We understand how to define interfaces and contracts that allow different components to evolve somewhat independently.",[24,55,57],{"id":56},"the-epic-drift-problem","The Epic Drift Problem",[10,59,60],{},"So I built a skill that helps break down large epics into smaller, independently deployable tasks right from the start. This seems like it should solve everything, right? Well, not quite. I discovered another problem that's more subtle but equally important. When AI breaks a large epic into multiple sub-tasks, each of those sub-tasks gets its own evaluation criteria and success conditions. Individually, each task can succeed perfectly according to its own criteria. But when you combine all the completed tasks together, they sometimes fail to achieve the original objective of the epic.",[10,62,63],{},"Think about what's happening here: as the AI works through each independent task, it can lose sight of the broader intent. Minor implementation details in early tasks can subtly shift the direction. Decisions made to satisfy one sub-task's success criteria can create inconsistencies with the overall goal. This phenomenon isn't unique to AI—it happens in regular human development too. You spec everything out, you build the first piece successfully, you build the second piece successfully, and then somewhere along the way you discover inconsistencies or realize that small decisions have compounded into a drift away from the original vision.",[24,65,67],{"id":66},"building-in-reconciliation","Building in Reconciliation",[10,69,70],{},"The solution I've been experimenting with involves building a reconciliation process directly into the workflow. Each sub-task needs to understand not just its own narrow objective, but also the broader epic and its specific role in achieving that epic's intent. This context awareness helps keep individual tasks aligned with the larger goal. But understanding the context at the beginning isn't enough—you also need periodic checkpoints where you reconcile current progress against the original intent.",[10,72,73],{},"Here's how this works in practice: as tickets progress through development, certain tasks might create contracts that other tasks rely on. After a few tickets are completed, there's a reconciliation phase where the system evaluates whether the implementation is still aligned with the epic's original intent. If minor implementation details have caused drift, the reconciliation process can identify those inconsistencies and adjust upcoming tickets accordingly. This creates a feedback loop that helps maintain alignment even as development uncovers new information and requirements evolve.",[10,75,76],{},"I want to be clear that this isn't a silver bullet. The results so far have been moderate—better than the naive approaches, but not perfect. The reconciliation process adds complexity and requires careful thought about when and how to perform those alignment checks. But it's producing decent results, and more importantly, it's addressing a real problem that I kept encountering in AI-assisted development.",[24,78,80],{"id":79},"the-bigger-picture","The Bigger Picture",[10,82,83],{},"This entire challenge—breaking down work effectively, maintaining epic alignment, creating truly independent deployable units—is fundamentally about maintaining sight of the forest while working on individual trees. It's easy to get tunnel vision, focusing so intently on the specific task in front of you that you lose track of the overall objective you're trying to achieve. This has always been a challenge in software development, but AI acceleration makes it more acute because we're moving faster and generating more code in less time.",[10,85,86],{},"The skills and practices we develop for AI-assisted development aren't separate from traditional software engineering principles—they're extensions and refinements of them. The same discipline that makes human engineers effective at decomposing work and maintaining architectural coherence is what we need to teach or embed into AI-assisted workflows. The difference is that what humans do intuitively through experience needs to be made explicit and systematic when working with AI.",[24,88,90],{"id":89},"lessons-learned","Lessons Learned",[10,92,93],{},"After working with agent orchestration systems and watching how AI transforms the software development lifecycle, I've come to appreciate that the real challenge isn't making AI write code faster—it's making AI understand the holistic context that experienced engineers bring to their work. It's not enough to break tasks into smaller pieces; those pieces need to be architected for independence from the start. It's not enough to define success criteria for individual tasks; those criteria need to ladder up to the broader objective in a way that's continuously validated.",[10,95,96],{},"The experimentation continues, and I expect these skills and techniques to evolve as I learn more about what works and what doesn't. But the core principle remains constant: see the forest, not just the trees. Understand the epic, not just the ticket. Build systems that maintain that broader context even as they work through granular details. That's the path toward making AI truly effective as a development partner rather than just a faster code generator.",{"title":98,"searchDepth":99,"depth":99,"links":100},"",2,[101,102,103,104,105,106,107],{"id":26,"depth":99,"text":27},{"id":36,"depth":99,"text":37},{"id":46,"depth":99,"text":47},{"id":56,"depth":99,"text":57},{"id":66,"depth":99,"text":67},{"id":79,"depth":99,"text":80},{"id":89,"depth":99,"text":90},"2026-06-09","AI writes fast but decomposes work badly — producing massive PRs and drifting from the original objective. Here's what I've learned about front-loading decomposition, avoiding the retrofit problem, and building reconciliation into agentic workflows to keep tasks aligned with the broader epic.","md",{},true,"/blog/forest-for-the-trees",{"title":5,"description":109},"blog/forest-for-the-trees","ilTy9AwbXTozNUQEgs_3Nzbw9hEE_5PZ-RBDWlb0TzI",1781100793452]