| Lately I’ve noticed coding agents getting significantly better especially at handling well-scoped, predictable tasks. It made me wonder: For a lot of Jira tickets especially small bug fixes or straightforward changes most senior developers would end up writing roughly the same implementation anyway. So I started experimenting with this idea: When a new Jira ticket opens: -It runs a coding agents (Claude/cursor) -The agent evaluates the complexity. If it’s below a configurable confidence it generates the implementation. -It opens a GitHub PR automatically. From there, you review it like any normal PR. If you request changes in GitHub, the agent responds and updates the branch automatically. So instead of “coding with an agent in your IDE”, it’s more like coding with an async teammate that handles predictable tasks. You can configure: -The confidence threshold required before it acts. -The size/complexity of tasks it’s allowed to attempt. -Whether it should only handle “safe” tickets or also try harder ones. It already works end-to-end (Jira → implementation → PR → review loop). Still experimental and definitely not production-polished yet. I’d really appreciate feedback from engineers who are curious about autonomous workflows: -Does this feel useful? -What would make you trust something like this? -Is there a self made solution for the same thing already created at your workplace? GitHub link here: https://github.com/ErezShahaf/Anabranch Would love to keep improving it based on real developer feedback. |
For trust, I'd want to see metrics on how often it gets the implementation "right enough" on first try vs. needs significant rework. The confidence threshold tuning sounds crucial - too conservative and it barely helps, too aggressive and you spend more time fixing than coding from scratch.
Have you tested it on tickets with ambiguous requirements? That seems like where it would struggle most, but also where the confidence evaluation becomes really important.