AI coding agent
Why it matters
For a business owner who builds with AI, the agent changes the bottleneck. Typing code is no longer the slow part. Describing the work clearly, and checking the result, is. That makes a marketer or founder able to ship a landing page tweak, an internal tool or a data clean-up that once needed a developer's week.
The catch is that an agent is confident whether it is right or wrong. It needs the same management as a fast junior colleague.
How to apply it
- Give it one bounded task with a clear definition of done, such as "the form saves the field and the existing tests still pass".
- Put standing instructions in a rules file so each session starts with the same conventions.
- Work on a separate branch under version control, so any change can be thrown away.
- Read the final change before it goes live, ideally as a pull request.
- Give it only the access the task needs. A test database is safer than the production one.
What it is
Early coding assistants worked like autocomplete: they predicted the next few lines while a person typed. An agent takes a goal such as "add a phone number field to the signup form and save it to the database" and works through the steps itself. It reads the relevant files, makes a plan, edits several files, runs the tests, reads the errors and tries again. Claude Code, Codex and the agent modes in editors such as Cursor are examples.
Common mistakes
- Handing over a vague goal like "improve the site" and expecting a sensible result.
- Merging changes without reading them because the tests passed.
- Letting the agent hold live credentials or customer data it does not need.
