---
title: "Give every open ticket its own agent"
description: "Turn the issue board into the queue for parallel agent runs."
url: https://starsling.dev/ci-playbooks/techniques/agent-per-ticket
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/agent-per-ticket
---

# Give every open ticket its own agent

Turn the issue board into the queue for parallel agent runs.

## How to choose an approach

This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it.

- **[OpenAI](https://starsling.dev/ci-playbooks/openai#agent-per-ticket)**: Use it when One engineer steers a few agent sessions by hand and the number of open tasks keeps growing. Check before adopting: More agents open more pull requests at once, so check runner capacity and queue time before raising the number of parallel agents. Adapted for GitHub Actions; compare the original system with yours.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Give every open ticket its own agent.

Turn the issue board into the queue for parallel agent runs.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/agent-per-ticket.md

Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence.
Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured.
Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo.
Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES.
Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch.
Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge.
````

## How OpenAI used this technique

Read what OpenAI changed and follow the original sources.

### OpenAI: Give every open ticket its own agent

**Translated to GitHub Actions**

- What OpenAI built: OpenAI's Symphony turns a project-management board into a control plane where every open task gets an agent.
- Your GitHub Actions version: An agent for every open ticket, with dependent tasks started only when they are unblocked.

Symphony turns a project-management board such as Linear into a control plane for coding agents. Every open task gets an agent, agents run continuously, and humans review the results. A planning task can generate a tree of dependent tasks, and agents start only the tasks that are not blocked, so independent work runs in parallel. Before Symphony, most people could comfortably manage three to five sessions at a time before context switching became painful. OpenAI reports a 500% increase in landed pull requests on some teams from Symphony as a whole.

Evidence: Documented technique

Use it when: One engineer steers a few agent sessions by hand and the number of open tasks keeps growing.

Before applying it: More agents open more pull requests at once, so check runner capacity and queue time before raising the number of parallel agents.

GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation.

[Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#agent-per-ticket)

[Primary source](https://openai.com/index/open-source-codex-orchestration-symphony/)

## CI optimization is continuous work.

Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on.

[Get self-improving CI](https://starsling.dev/products/runners)

## Company Playbooks

### [Anthropic](https://starsling.dev/ci-playbooks/anthropic)

Run only affected tests

### [DoorDash](https://starsling.dev/ci-playbooks/doordash)

Run build and test in parallel, sharded across CI agents

### [Linear](https://starsling.dev/ci-playbooks/linear)

Benchmark the same job on faster hardware

### [Mastra](https://starsling.dev/ci-playbooks/mastra)

StarSling runners for the slowest test workflow

### [OpenAI](https://starsling.dev/ci-playbooks/openai)

Rank CI fixes by the minutes they save

### [Ramp](https://starsling.dev/ci-playbooks/ramp)

Move a CPU-bound test suite to a faster CPU

### [Shopify](https://starsling.dev/ci-playbooks/shopify)

Let an agent iterate against one CI metric

### [Spotify](https://starsling.dev/ci-playbooks/spotify)

Build arm64 on native arm64 runners

### [Stripe](https://starsling.dev/ci-playbooks/stripe)

Run only the tests a change can reach

### [Uber](https://starsling.dev/ci-playbooks/uber)

Build the merge-queue speculations most likely to be needed