In an earlier post I wrote that code communicates to the computer and to future readers. With AI coding agents, there’s a third audience: the agent itself. The agent reads your code to understand how to extend it. Good architecture makes this communication clearer. Bad architecture makes the agent confidently generate more bad code.

AI agents are very good at using well-designed components. They are not very good at designing them. They can implement against a clear interface, follow established patterns, and generate code that fits into an existing structure. They struggle with deciding what the interfaces should be, knowing which abstractions will age well, and understanding the domain deeply enough to decompose it correctly.

AI coding agents can move fast. The constraint on their productivity is correctness. They are always confident, but they need clear, automatic signal about correctness otherwise a human ends up providing all of that signal (slowly). A robust test suite can ensure the confidence aligns with correctness. The shape of the test suite matters as much as its existence.

Interface tests vs internal tests

Tests that cover publicly exposed interfaces without depending on internal implementation details are a force multiplier for AI agents (and humans). These tests define what correct behavior looks like without dictating how that behavior is achieved. An agent can refactor freely, restructure internals, rewrite implementations entirely—and as long as the tests stay green, the changes are probably safe. A “perfect” test suite would cover the entire set of visible behavior, so a green suite would mean correct software. Don’t let the difficulty of a perfect suite prevent building a good one, and consider how lower development costs and higher ROI on tests may mean aiming closer to “perfect” than you might have once.