Backend developers
AI coding tools for backend developers
Backend work is where AI coding agents pay off most reliably, because the change usually spans several files at once — a schema, a migration, a handler and its tests. That is the shape an agent handles and an autocomplete cannot. The recipes below cover eight language stacks plus API and database patterns.
What actually changes
The unit of work moves from a function to a change. A migration, the model it alters, the handler that reads it and the test that covers it are one task for an agent, and four separate context switches for a person with autocomplete.
The review burden moves with it. An agent that runs the test suite and iterates until it passes hands you a diff that already compiles — which makes the remaining question "is this the right change" rather than "does this work".
Recipes for your stack
These are documentation pages. Some are free to read; the rest are part of the subscription.
Where you stand
Twenty-five questions on how production-grade your AI engineering is, with a plan at the end. Free, no account.
Frequently asked questions
Which tool is best for backend work?
All three edit many files autonomously, run commands and self-correct by running tests, so none is disqualified. Claude Code adds hooks and /goal for scripted, supervised runs; Cursor adds visual hunk-by-hunk review; Codex adds native GitHub PR reviews.
Can an agent write the migration and its test in one pass?
That is the case they are built for. Autonomous multi-file editing, command execution and self-correction by running tests are core capabilities in all three tools, which is why a schema change and the test covering it are one task rather than four.