Two years ago, if you'd told most engineering leaders that AI would be writing production functions, refactoring legacy modules, and catching bugs before a human ever looked at them, they'd have called it a productivity miracle in the making. And in a narrow sense, it has been. AI coding assistants are genuinely excellent at typing code fast.
How is it possible that AI calmly fixes a completely different function than the one you meant and is fully confident it did exactly what you asked? The answer is called hallucination!
Today, AI-assisted software development often follows a straightforward process:
Each testing level plays a specific role in software development and answers a distinct question:
We expect unit tests to verify the correctness of modified logic and help catch unintended changes in application behavior. When developing with AI, it is also essential to check that functionality outside the task's scope remains intact.
The quality of an AI agent's result is decided by the quality of the prompt. Here is the exact 5-block structure AITM uses to write one.
Why AI coding agents struggle the moment they touch an existing codebase — and how AITM's pipeline feeds them the missing context.
Why splitting a big AI task into small, independently verified steps beats one giant prompt — and how AITM's task chains enforce it.
Why AI coding agents struggle on existing codebases, and what AITM's pipeline does about it.