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Optio

Orchestrate AI coding agents in K8s to go from ticket to PR

Details

External ID
47520220
Source
HN
Company
—
Product
Optio
Website domain
github.com
Launched
March 25, 2026
Cohort
—
Upvotes
88
Upvotes percentile
0.8929889298892989
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

I think like many of you, I've been jumping between many claude code/codex sessions at a time, managing multiple lines of work and worktrees in multiple repos. I wanted a way to easily manage multiple lines of work and reduce the amount of input I need to give, allowing the agents to remove me as a bottleneck from as much of the process as I can. So I built an orchestration tool for AI coding agents:Optio is an open-source orchestration system that turns tickets into merged pull requests using AI coding agents. You point it at your repos, and it handles the full lifecycle:- Intake — pull tasks from GitHub Issues, Linear, or create them manually- Execution — spin up isolated K8s pods per repo, run Claude Code or Codex in git worktrees- PR monitoring — watch CI checks, review status, and merge readiness every 30s- Self-healing — auto-resume the agent on CI failures, merge conflicts, or reviewer change requests- Completion — squash-merge the PR and close the linked issueThe key idea is the feedback loop. Optio doesn't just run an agent and walk away — when CI breaks, it feeds the failure back to the agent. When a reviewer requests changes, the comments become the agent's next prompt. It keeps going until the PR merges or you tell it to stop.Built with Fastify, Next.js, BullMQ, and Drizzle on Postgres. Ships with a Helm chart for production deployment.

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
—
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
ai coding agents orchestration for kubernetes
Manually corrected
False

Could you build this?

Partial While orchestrating CLI agent commands and polling tickets can be vibe-coded, managing dynamically spawned Kubernetes sandboxes, container isolation, persistent worktrees, and robust error recovery requires real DevOps and distributed systems engineering.

What it would actually take: A production version requires a Kubernetes operator or control plane using client-go or K8s API to provision isolated, ephemeral runner pods per branch/task. Key technical hurdles include safe container isolation, dynamically mounting/syncing git worktrees, handling OOM/timeout states, and maintaining bidirectional agent-to-PR communication. It requires substantial Kubernetes systems architecture and cloud security expertise.

Discussion

20 comments analyzed.

Competitors mentioned: Playwright (for testing/validation), GitHub Actions (CI/CD integration)

Concerns raised: Agents get stuck in retry loops without recognizing circular failures, Auto-merge without human review can break production systems, Agents declare success on mistakes without proper validation, Quality degrades over time on active repositories without frequent steering, Agents take shortcuts and avoid thorough testing when bored

Feature requests: Retry limits before escalation to humans, Better validation approach to catch agent mistakes automatically, Agent-to-agent review of outputs and prompt improvement, Isolated/headless mode with proxy for safety, Separation of planning and execution artifacts clarity

Competitors

Other products that read as similar to this one — 290 launches clear the similarity bar, closest 8 shown.

Attention rank: #31 of 291 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 144 days after the earliest competitor.

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