An AI agent is a system that can plan a multi-step task, use tools and evaluate intermediate results. In software work, that can mean reading a repository, proposing a change, running tests and revising the change.
From autocomplete to a work loop
Traditional code completion responds to a small local prompt. An agent works across a larger loop: understand the goal, inspect relevant files, choose an action, observe the result and decide what to do next. The loop is useful because software tasks are rarely solved by one line in isolation.
Where agents help
Agents are good at repetitive exploration, generating test cases, explaining unfamiliar code and preparing a first draft of a small feature. They can also make routine migrations faster when the desired transformation is precise and the project has strong automated checks.
Why review still matters
An agent can misunderstand a requirement, use an outdated library pattern or produce code that passes a narrow test but breaks a wider contract. Humans still need to set boundaries, review the diff, check security-sensitive behavior and decide whether the result is maintainable.
A practical workflow
Give the agent one concrete objective, point it toward the nearest relevant code, ask for a focused validation, and review the smallest possible change. Treat generated output as a proposal until it has passed tests and human inspection.