Developer assistants and code generation.
How to choose · AI coding tools
Evaluate coding assistants on how they treat your source code, how well they understand your repository, where they run in the workflow, and how their output fits review and testing. Compare code retention and training terms, repository-aware context, editor and pipeline integration, administrative policy controls, licence-related filters, and measured effect on review burden rather than raw lines produced.
Comparison · AI coding tools
Assistive tools help inside the developer's flow with small, visible changes. Autonomous agents take a task description, edit many files, run commands and return a result for review. Agents suit well-bounded chores with strong tests; assistive tools suit exploratory and design-heavy work. The deciding factors are test coverage, review capacity, permission risk and how clearly the task can be specified.
Problems & fixes · AI coding tools
Generated code often looks right while hiding issues: insecure defaults, missing input validation, nonexistent or outdated dependencies, silent error swallowing, copied licensed snippets and tests that assert nothing. Catch these with the same safeguards used for human code: review, static analysis, dependency checks, meaningful tests and secret scanning, applied with extra suspicion toward confident-looking output.
Requirements · AI coding tools
A workable policy names approved tools and plans, forbids pasting secrets or customer data, sets repository eligibility, requires normal review and testing for all code regardless of origin, defines licence and provenance handling, covers agentic tool permissions, and assigns ownership for monitoring and updates. Keep it short enough that developers actually read it.