What is AI engineering?
A professional software-development practice in which experienced developers use AI agents through terminals, IDEs, repositories, and delivery systems while retaining responsibility for architecture, quality, security, and production outcomes.
Why is this important?
AI engineering captures the leverage of coding agents without treating generated output as automatically correct. It shifts developer effort toward specification, orchestration, review, testing, system design, and operational judgment.
How it works
A developer gives an agent a bounded task and repository context, reviews its plan, lets it inspect and modify code, runs deterministic checks, evaluates the diff, and iterates until engineering acceptance criteria are met.
Technical example
An engineer delegates a database migration to Claude Code, reviews the schema plan, runs tests in an isolated branch, examines rollback behavior, and approves the change only after production safeguards pass.
Implementation notes
Use repository instructions, isolated environments, least-privilege credentials, protected branches, reproducible tests, observability, review gates, and clear ownership. AI changes the interface to engineering, not the accountability standard.
