---
title: "Start an agent as a suggestion and put it on autopilot once it has proven reliable"
description: "Teams ask the agent for suggestions, observe its performance, add guidance, and automate it after it has proven reliable."
url: https://starsling.dev/ci-playbooks/techniques/advisory-before-required
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/advisory-before-required
---

# Start an agent as a suggestion and put it on autopilot once it has proven reliable

Teams ask the agent for suggestions, observe its performance, add guidance, and automate it after it has proven reliable.

## 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.

- **[Linear](https://starsling.dev/ci-playbooks/linear#advisory-before-required)**: Use it when A new agent check, or an agent that opens its own pull requests, is about to become a required gate. Check before adopting: Set the promotion bar before the trial starts, such as the share of its findings that people acted on, and keep a person's approval on the merge.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Start an agent as a suggestion and put it on autopilot once it has proven reliable.

Teams ask the agent for suggestions, observe its performance, add guidance, and automate it after it has proven reliable.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/advisory-before-required.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 Linear used this technique

Read what Linear changed and follow the original sources.

### Linear: Start an agent as a suggestion and put it on autopilot once it has proven reliable

Linear's teams introduce autonomy gradually: they ask the agent for suggestions, observe its performance, and add guidance, and they put it on autopilot once it has proven reliable. Engineers also get better results by breaking work into small, focused steps for the agent. For an agent check on pull requests, that is a check that comments first and becomes a required check after its record has been read.

Evidence: Documented technique

Use it when: A new agent check, or an agent that opens its own pull requests, is about to become a required gate.

Before applying it: Set the promotion bar before the trial starts, such as the share of its findings that people acted on, and keep a person's approval on the merge.

[Read more in the Linear playbook](https://starsling.dev/ci-playbooks/linear#advisory-before-required)

[Primary source](https://linear.app/now/how-we-use-linear-agent-at-linear)

## 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