AI-generated code lands in the technical debt quadrant nobody chose. Where it forms, why review can’t catch up, and the generation-time rules that prevent it.
Rupashri Gulawani
Reviewing AI-generated code is not one read done harder. It is five: functional, regression, security, budget, maintainability. The framework, and how to run all five without slowing merges.
The test suite passed and CI was green, yet production broke. Why AI-assisted code slips past pipelines built for human-paced change, and what verification has to become.
AI speeds up coding, not delivery. What actually gets faster, where a software estimate really goes, and how to set a timeline you can take to your board.
Most AI workflows treat the code as if it were the theory. On a brownfield system it is not. Codebase archaeology is the discipline that produces the written understanding an agent needs before it edits.
An AI coding agent will write confident code against assumptions it invented. On a 12-year-old codebase, that is where the bugs come from. What the agent has to understand first, and how to brief it.
Most companies are paying an invisible tax on their AI initiative. It isn’t the pilot. It’s the evaluation phase. A short read for senior leaders who already know which workflow they would automate first, but haven’t said it out loud yet.
Agentic AI does not have to be a multi-quarter program. Here is what we tell every CTO before the first line of agent code is written, and why the first production agent can ship in weeks when the right decisions are made in advance.
88% of AI pilots never reach productionIDC 40%+ of agentic projects to be canceled by 2027Gartner 171% average ROI when…
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