This guide provides general evaluation criteria. Product features, policies and prices can change; verify provider-specific details at the source before making an important decision.
Control repository and secret access
Start with the smallest repository and permission scope that can complete the task. Do not expose production credentials, private keys or unrelated repositories. Check what the extension or agent can read, write and execute, especially when it can run shell commands or open network connections.
Use test environments and disposable branches for unfamiliar agent workflows. The easier it is to undo a change, the safer it is to experiment.
Require readable diffs
A coding assistant should make it easy to understand what changed and why. Large opaque edits are harder to review and more likely to hide regressions. Prefer small commits or checkpoints, and ask the tool to explain assumptions when it changes architecture, dependencies or security-sensitive code.
Do not accept generated code merely because it compiles. Review error handling, authentication, authorization, logging, data validation and failure behavior.
Use tests as a gate, not decoration
Run the existing test suite before and after the change. Add tests that cover the behavior the agent is modifying, including negative cases. If the repository has no useful tests, the risk of autonomous changes is much higher and human review needs to be correspondingly stronger.
Static analysis, type checking, dependency scanning and linters can catch classes of mistakes that a conversational review misses.
Check dependencies and licenses
Generated code can introduce packages that are unnecessary, abandoned, insecure or incompatible with your license requirements. Review every new dependency and pin versions according to your project policy. For copied snippets or model-generated code, keep the same licensing and provenance standards you would use for human contributions.
A tool that saves minutes during generation can create hours of maintenance if it expands the dependency surface without a clear reason.
Measure reviewable productivity
Track time from request to reviewed, tested merge—not time to first code output. The right tool should reduce total cycle time while keeping the code understandable. Evaluate it on representative tasks such as bug fixes, refactors, tests and documentation rather than a single demo.
For production systems, keep a human accountable for the merge. AI can propose and execute changes, but responsibility for security and correctness remains with the team.