From vibe coding to a pipeline: making AI coding agents stop starting from zero [ukr]

As a codebase grows, AI coding agents get more expensive and less reliable. Every session re-learns the architecture, conventions drift, and fixes break other fixes. The usual response is to switch to a bigger model, which raises the bill and leaves the cause in place. I went through this on my own product: from a frontend fully delegated to an agent and a bill of over $1,000 a month to a pipeline where the agent works from a spec instead of chat history.

We'll cover:
• - how to move rules, knowledge and verification out of chat and into the repository, so sessions become disposable;
• - three attempts at an in-repo knowledge base, and why a stale doc is worse than no doc;
• - the pipeline from task to push, and why the spec matters more than the prompt;
• - why agent permissions must be enforced limits, not polite requests in a rules file, and why everything an agent reads is a potential instruction.

At the end I'll share an open playbook with rules you can drop straight into your own repo.