I'm working on something a little similar but mines more a dev tool vs process automation but I love where yours is headed. The biggest issue I've run into is handling retries with agents. My current solution is I have them set checkpoints so they can revert easily and when they can't make an edit or they can't get a test passing, they just restart from earlier state. Problem is this uses up lots of tokens on retries how did you handle this issue in your app?
FWIW, a "cheaper" version of this is triggering Claude via GitHub Actions and `@claude`ing your agents like that. If you run your CI on Kubernets (ARC), it sounds pretty much the same
Looks cool, congrats on the launch. Is there any sandbox isolation from the k8s platform layer? Wondering if this is suitable for multiple tenants or customers.
Oh good question, I haven't thought deeply about this.<p>Right now nothing special happens, so claude/codex can access their normal tools and make web calls. I suppose that also means they could figure out they're running in a k8s pod and do service discovery and start calling things.<p>What kind of features would you be interested in seeing around this? Maybe a toggle to disable internet connections or other connections outside of the container?
And what stops it making total garbage that wrecks your codebase?
There are a few things:<p>a) you can create CI/build checks that run in github and the agents will make sure pass before it merges anything<p>b) you can configure a review agent with any prompt you'd like to make sure any specific rules you have are followed<p>c) you can disable all the auto-merge settings and review all the agent code yourself if you'd like.
> to make sure<p>you've really got to be careful with absolute language like this in reference to LLMs. A review agent provides no guarantees whatsoever, just shifts the distribution of acceptable responses, hopefully in a direction the user prefers.
Fair, it's something like a semantic enforcement rather than a hard one. I think current AI agents are good enough that if you tell it, "Review this PR and request changes anytime a user uses a variable name that is a color", it will do a pretty good job. But for complex things I can still see them falling short.
Ticket -> PR -> Deployment -> Incident
What’s the most complicated, finished project you’ve done with this?
Recently I used to to finish up my re-implementation of curl/libcurl in rust (<a href="https://news.ycombinator.com/item?id=47490735">https://news.ycombinator.com/item?id=47490735</a>). At first I started by trying to have a single claude code session run in an iterative loop, but eventually I found it was way to slow.<p>I started tasking subagents for each remaining chunk of work, and then found I was really just repeating the need for a normal sprint tasking cycle but where subagents completed the tasks with the unit tests as exit criteria. So optio came to my mind, where I asked an agent to run the test suite, see what was failing, and make tickets for each group of remaining failures. Then I use optio to manage instances of agents working on and closing out each ticket.
the misaligned columns in the claude made ASCII diagrams on the README really throw me off, why not fix them?<p>|
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