There are several kinds of AI-assisted coding
People use “vibe coding” to describe everything from playful prototyping to handing a large change to an agent. Those activities need different review standards. An experiment used once by its author has a different responsibility boundary from an application handling other people’s data.
I prefer to name the workflow precisely: chat-based advice, IDE completion, an agent editing local files, or a cloud agent working on a branch. That tells a team where changes happen, which tools can run and what evidence is available for review.
Coding agents make the development loop explicit
GitHub’s cloud agent documentation describes repository research, planning and code changes on a branch, with opportunities to inspect the diff and iterate. This is broader than getting a snippet in a chat window. The agent can work through part of the development process, but the repository still needs clear instructions and an acceptance boundary.
A useful assignment describes the concrete problem, desired behavior and checks that demonstrate it. “Make this premium” leaves a lot to guess. “Keep the existing routes, improve the mobile hero, preserve reduced motion and verify the build” gives the work a reviewable shape.
Primary sourceThe fastest safe loop has a small scope
Start with a narrow change. Read the diff, run the relevant checks and interact with the result. Expand after the first outcome is understood. Avoid asking for unrelated refactors while fixing a bug; a large diff can make the original regression harder to identify.
Tests should establish behavior, not repeat the implementation in another file. For an AI comparison, check that the job’s requirements remain literal and that invalid inference produces an error. For a theme toggle, inspect startup, persistence and reduced motion in the browser. Passing compilation does not establish these behaviors.
Read the diagram as text
The fastest safe loop has a small scope — Start with a narrow change. Read the diff, run the relevant checks and interact with the result. Expand after the first outcome is understood. Avoid asking for unrelated refactors while fixing a bug; a large diff can make the original regression harder to identify.
Permissions and context are part of the tool
An agent with repository access can see more than the file you are thinking about. Keep secrets out of committed files, define which operations are allowed and inspect dependencies before accepting them. Review an agent’s claims about tests against the actual command output.
GitHub’s responsible-use guidance discusses limitations and the need to review agent-generated work. I would extend that habit to every coding tool: treat the output as a contribution that must meet your project’s standards. A confident explanation is useful only when the changed code supports it.
Primary sourceWhat I want from the future
I want tools that make it easier to understand a codebase, preserve completed work and turn a clear requirement into a small verified change. Fast generation is part of that, but continuity and honest failure handling matter just as much.
Learn enough of the underlying stack to diagnose what the agent missed. Ask it to explain tradeoffs, then check those explanations against the code and primary documentation. Keep a recoverable version before a broad edit.
AI-assisted development can lower the cost of trying an idea. Software engineering is still the work of deciding what should exist, proving that it behaves correctly and taking responsibility when it does not.
The sources behind the ideas.
Research and primary documentation for the factual points above. Examples and recommendations are my interpretation.
