alekstret 3 hours ago
First of all, the agent writes code. After that, it creates tests and verifies that all of them work correctly by cracking the checks and rerunning the test suite.
Secondly, it gives me the ready to test code + set up environment (stage). I'm checking that the code actually works, and if not, we are making some fixes until I'm fully satisfied with the result.
Thirdly, the agent starts an external review using skill for code review and usually makes some additional fixes to codebase and corrects tests again.
After all, we are ready to commit and push the feature to the main branch. Before merging, we start CI/CD - which is important because it does a type check - and wait until the run finishes successfully.
That's how it happens in my case.
Quick note: this plan was built after many iterations of coding and testing, and it has finally proved that it works.
knighthacker 4 hours ago
Disclosure: I'm building AQ (aq.dev), which is partly why I'm deep in this. We wrote up the session-review practice here: https://aq.dev/guides/how-to-review-an-ai-coding-session/. The practice works with any agents too, nothing tool-specific about it.
jmathai 3 hours ago
I have found a key is to use end-to-end tests and not unit tests.
This has been working well so far after around 100 pull requests for an app I recently decided to make. So far it’s been good.
It’s free, no subscription.
I’ve done similar with backend projects utilizing GitHub actions to run tests and publish to staging for verification.
mackatsol 5 hours ago
mr1337 6 hours ago
This actually makes the LLMs better at coding as well. They can verify their own results and iterate with far less manual validation.
rk007 6 hours ago