AI coding assistants suggest code, explain unfamiliar files and increasingly carry out multi-step tasks across a repository. Teams see the biggest gains on routine work — tests, boilerplate, migrations — while code review and automated testing remain essential safeguards.
AI agents go beyond answering questions: they combine a language model with tools, memory and a decision loop to plan multi-step tasks, act, observe results and adjust. They work best on well-defined tasks, and human oversight remains essential for high-impact actions.