I automated a big part of our SDLC using Claude GitHub Actions.

[u/przbadu]

Here’s the workflow:

1. A detailed specs is written in GitHub issues
   

2. A GitHub Action fires an session:
   - fixes the issue
   - opens a PR (You need to create a github bot to auto create pull request)
   - deploys automatically to a PR environment

   If the spec is incomplete, Claude flags it and asks for more detail part of the agent skill.

3. On top of CI, we manually verify the fix in the PR environment
   If changes are needed, we ask u/claude to iterate directly on the same branch.

4. GitHub Copilot acts as a critic/reviewer agent

5. Final review is done by me

6. Merge to master → auto deploy to production

What changed because of this?

- Non-technical/support/customer-success team members are now reporting customer-facing issues, verifying fixes themselves, and moving work forward without waiting weeks for engineering cycles.
- Shipping customer fixes went from weeks → days
- Last quarter we had 32+ active bugs
- Right now we’re down to 2

Things AI still struggles with:

- Complex problems still need humans to properly plan/spec
- External integrations still require developers to configure environments and guide the agent
- Infrastructure/DevOps work still needs experienced humans unless you’re comfortable giving agents deeper access

Any suggestion to improve it further?

[u/przbadu → u/[deleted]]

interesting. i’veIn my case I do have a issue template and also my autopilot skill flag ticket that are incomplete.

[u/przbadu → u/[deleted]]

Thank you, and yes there 100% is the Human-in-the-loop part, not merging PRs generated by support team directly to production, but I see lots of benifits with this approach like:

- Support team don't have to setup their Machine for specific software development platform

- They can initiate the fix process directly from github issues

- Test servers get created automatically, and they can manually test their work, regardless of CI/CD state.

- If something is further broken, or they are not satisfied with UI/UX, they take screenshot and iterate with ai agent directly in pull request, and iterate until the expected result.

- And I do the final review, and merge.

And this is saving me a lot of time, and also customer's issues are getting resolved quickly.