Show HN: Kelos – Define your AI coding agent workflow as YAML on Kubernetes

[gjkim042]

Kelos is a Kubernetes framework for orchestrating autonomous AI coding agents (like Claude Code) in ephemeral pods.

The original goal was sandboxing — I didn't want to run agents with --dangerously-skip-permissions locally. But the real unlock turned out to be declarative workflows. You define agent tasks as Kubernetes CRDs, things like:

- Watch for "bug" issues → auto-draft a fix PR

- Auto-review incoming pull requests

- Auto-triage new issues with labels and priority

- Periodically scan the codebase → propose improvements

- Test the project as a new user → surface rough edges

Self-development pipeline: https://github.com/kelos-dev/kelos/tree/main/self-developmen...

I've been using Kelos to develop Kelos. When something breaks, I refine the YAML or add features to the controller. It's early and rough around the edges, but the core loop works.

Happy to answer questions about the design or what's broken.

[jlongo78]

Interesting approach. One pattern I've seen work well with YAML-defined agent workflows is keeping your session state observable alongside the orchestration config. When agents run in parallel, the hardest debugging problem isn't the workflow definition itself but correlating what each agent actually did across concurrent runs. Whatever tooling you use, prioritize being able to replay and search past agent output by context, not just timestamps. That audit trail saves hours.

[gjkim042]

Thanks,

Actually, Kelos captures structured outputs after each coding task compeletes: the final branch, the created PR link if exists, input/output tokens, and so on. And coding agent’s logs would remain after finished as a container log.

Is theere anything you want to add additionally?