A practical guide to actually working well with AI coding agents

[SuperOwen35]

A widely shared guide argues most people misuse AI coding agents by giving vague instructions and then blaming the model when the result is a pile of code nobody actually wanted. The author, an experienced practitioner, distinguishes three tiers, simple autocomplete, assistants that answer direct requests, and true agents that can inspect a repository, create and modify files, run tests, and repeat the process until a goal is achieved, arguing the loop itself is what makes agents fundamentally different and more powerful

The core recommended workflow is Ask, Inspect, Plan, Implement, Test, Review, starting by asking the agent to inspect the repository and identify affected files and existing tests without changing anything yet, which catches misunderstandings before 15 files get modified based on a wrong assumption. The piece stresses that context matters more than clever prompting, pointing the agent to specific files, existing documentation and error handling patterns produces dramatically better results than a vague instruction alone, and that breaking work into small, individually testable tasks makes failures easy to locate

The author's closing argument is that programmers are shifting from writing code to directing, assessing and coordinating code generating systems, and that this doesn't reduce the importance of programming, it makes understanding software more important since producing code is now cheap but knowing what it should do and whether it should exist at all remains the genuinely hard part. Curious what people think about this shift, does good agent delegation actually require the same underlying skills as good traditional software engineering, or a genuinely different skill set

How to Work with AI Coding Agents | Towards Data Sciencetowardsdatascience.com