The Best Tasks to Give an AI Coding Agent

Tasks with a clear definition of done

Agents do best on work where success is unambiguous and checkable: a failing test that needs to pass, a type error that needs to go away, a lint rule that needs to be satisfied across the codebase. The feedback loop does the hard work of steering the agent toward a correct answer.

This is also why migration-style tasks fit well, such as updating a deprecated API call across fifty files or renaming a symbol consistently. The pattern repeats, the correctness bar is objective, and a human reviewing fifty similar diffs is exactly the kind of tedious work worth delegating.

Well-scoped, low-ambiguity features

Small, self-contained features with a clear spec, like adding a field to an existing form and threading it through to the API and database, play to an agent's strength of following an established pattern in the codebase rather than inventing an architecture from scratch.

Test writing is another strong fit, especially backfilling coverage for existing, unchanged code. The agent can read the implementation and generate reasonable test cases without needing to make any judgment calls about new behavior.

• Fixing a specific failing test or type error
• Mechanical, repeated changes across many files
• Dependency upgrades and the resulting breakage
• Backfilling test coverage for existing code
• Small features that mirror an existing pattern

A rule of thumb

If you could describe the task in two sentences and a competent engineer unfamiliar with your business context could execute it correctly from that description alone, it is probably a good fit for an agent. If the task depends on judgment calls that only someone steeped in the product would get right, it is not.

Start there, watch how the agent performs, and expand the scope of what you delegate as your confidence in its output on your specific codebase grows.