GitHub’s November 6 guide argues that coding agents affect more than implementation: developers still gather requirements, plan changes, review diffs, evaluate tests, and investigate production behavior. It organizes the workflow into Plan, Build, Verify, and Observe. The article also cites company usage data and an external study about review time; those figures are not a controlled measurement of every team’s productivity and should be read with their source and methodology in mind.

The durable point is that output volume and useful delivery are not the same thing. An agent can produce code quickly while increasing review load, duplicating changes, or missing product requirements. Teams need acceptance criteria and observable checks that connect an implementation to user behavior. For front-end work, that often means covering responsive states, accessibility, loading and error cases, and the visual result—not just passing a unit test.

For career development, build skills that help you set direction and judge results: decomposition, system context, debugging, risk assessment, and communication. Keep a record of outcomes rather than counting prompts or generated lines. A small portfolio showing a feature taken from requirements through production is stronger evidence than claiming general “AI fluency.”