Every codebase is terrain. Some are smooth highways, where AI agents can move fast and confidently. Others are more like an obstacle course - still functional, but harder to navigate, even for experienced developers. For AI agents, the difference matters more than you might think.

Matt Pocock recently tweeted, “Know the ground your agent will walk on.” It’s a great metaphor. AI coding assistants aren’t just tools - they’re travelers trying to make sense of your landscape. The easier that terrain is to read, the better they perform.

The Terrain Metaphor

Think of your AI agent as a sharp, capable junior developer. Fast, tireless, and helpful - but very literal. They don’t infer intent. They follow cues.

When your codebase has clear structure with focussed models, controllers that follow consistent patterns, logic that lives in obvious places then AI agents can hit the ground running. They know where to go and what to do. But when logic is scattered across models, helpers, and controller actions - when responsibilities blur and patterns break - it’s harder. The AI has to guess, and that’s when bugs, duplication, or missed edge cases creep in.

You’ve likely seen it: in a clean, readable codebase, the AI knows where to add password reset logic. In a tangled one, it might reinvent validation from scratch, or break something that silently relied on old behavior.

The Productivity Multiplier

Well-structured code doesn’t just help AI a little. It can make them drastically more useful.

Clean abstractions give the model leverage. Instead of spitting out code you need to carefully review or fix, it can offer changes that fit right into your architecture. The AI stops being just a helpful autocomplete and starts being a real multiplier.

The Tradeoffs Are Real

Of course, writing clean code takes time. If you’re in a startup sprinting to market, you might favor speed over structure. Sometimes that’s the right call. In those cases, letting your code get a little messy isn’t laziness but a conscious tradeoff. You’re buying flexibility today and deferring cleanup for later. That’s valid.

But at some point, the cost flips:

AI-generated code takes longer to fix than to write yourself
You spend more time explaining context than the AI saves
The model’s confusion mirrors your own team’s friction
Onboarding anyone—human or AI—starts to hurt

That’s the inflection point where a bit of codebase tidying pays dividends.

For the Skeptical Developer

Maybe you’ve been coding 20 years without ever needing an AI assistant. Why restructure your entire codebase now? You might not need to. You already understand the context behind your code’s quirks. You know why that oddly-named method is doing three things. You’ve internalized its history. AI doesn’t get that. It only sees what’s explicitly in front of it. In a way, that’s a feature, as it reveals the true complexity of your system, without leaning on tribal knowledge or personal memory.

This can be frustrating. “The AI doesn’t get it,” you might think. But maybe it’s not just the AI. Maybe your code could be clearer for both machines and for future humans.

Start Small

This isn’t a call for massive rewrites. Just test the idea in small ways:

Pick a boundary: If a controller handles validation and business logic, extract just one part.
Name with intention: Rename ".call" to something more specific, even if it sounds boring.
Untangle workflows: A method doing five things might actually be a hidden service. Make it explicit.
Run a test: Ask your AI agent to modify the cleaned-up area. See what changes.

Often, you’ll notice a sharp difference in output quality. Clean code tends to pull in more of the same. And AI tools tend to follow the patterns you establish - good or bad.

A Useful Mirror

If an AI assistant keeps stumbling in your project, it’s not necessarily a sign the tools are flawed. It might be a signal. An opportunity to make your code more legible, more modular, more intentional.

Yes, some worry that we’re letting tools dictate how we design. But that’s not quite it. We’re designing for clarity, and clarity helps everyone. AI just happens to be very sensitive to it.

Looking Ahead

The developers who thrive in the AI age won’t be the ones who fight the tools, or the ones who blindly trust them. It’ll be the ones who learn to work alongside them.

Your codebase is the ground. Every design decision you make shapes that landscape. The best developers have been building clear, navigable codebases for years - not for AI, but for other people. That clarity now pays dividends twice.

You don’t have to refactor everything. But you can start laying better ground.