GitHub Agentic Workflows now in Technical Preview ✨

Automate repository tasks with GitHub Agentic Workflows

Discover GitHub Agentic Workflows, now in technical preview. Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.

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Imagine visiting your repository in the morning and feeling calm because you see:

• Issues triaged and labelled
• CI failures investigated with proposed fixes
• Documentation has been updated to reflect recent code changes.
• Two new pull requests that improve testing await your review.
• All of it visible, inspectable, and operating within the boundaries you’ve defined.

That’s the future powered by GitHub Agentic Workflows: automated, intent-driven repository workflows that run in GitHub Actions, authored in plain Markdown and executed with coding agents. They’re designed for people working in GitHub, from individuals automating a single repo to teams operating at enterprise or open-source scale.

At GitHub Next, we began GitHub Agentic Workflows as an investigation into a simple question: what does repository automation with strong guardrails look like in the era of AI coding agents? A natural place to start was GitHub Actions, the heart of scalable repository automation on GitHub. By bringing automated coding agents into actions, we can enable their use across millions of repositories, while keeping decisions about when and where to use them in your hands.

GitHub Agentic Workflows are now available in technical preview. Check out our blog post and we’ll explain fully what they are and how they work.

We invite you to put them to the test, to explore where repository-level AI automation delivers the most value.

AI repository automation: A revolution through simplicity

The concept behind GitHub Agentic Workflows is straightforward: you describe the outcomes you want in plain Markdown, add this as an automated workflow to your repository, and it executes using a coding agent in GitHub Actions.

This brings the power of coding agents into the heart of repository automation. Agentic workflows run as standard GitHub Actions workflows, with added guardrails for sandboxing, permissions, control, and review. When they execute, they can use different coding agent engines—such as Copilot CLI, Claude Code, or OpenAI Codex—depending on your configuration.

The use of GitHub Agentic Workflows makes entirely new categories of repository automation and software engineering possible, in a way that fits naturally with how developer teams already work on GitHub. All of them would be difficult or impossible to accomplish traditional YAML workflows alone:

1. Continuous triage: automatically summarize, label, and route new issues.
2. Continuous documentation: keep READMEs and documentation aligned with code changes.
3. Continuous code simplification: repeatedly identify code improvements and open pull requests for them.
4. Continuous test improvement: assess test coverage and add high-value tests.
5. Continuous quality hygiene: proactively investigate CI failures and propose targeted fixes.
6. Continuous reporting: create regular reports on repository health, activity, and trends.

These are just a few examples of repository automations that showcase the power of GitHub Agentic Workflows. We call this Continuous AI: the integration of AI into the SDLC, enhancing automation and collaboration similar to continuous integration and continuous deployment (CI/CD) practices.

GitHub Agentic Workflows and Continuous AI are designed to augment existing CI/CD rather than replace it. They do not replace build, test, or release pipelines, and their use cases largely do not overlap with deterministic CI/CD workflows. Agentic workflows run on GitHub Actions because that is where GitHub provides the necessary infrastructure for permissions, logging, auditing, sandboxed execution, and rich repository context.

In our own usage at GitHub Next, we’re finding new uses for agentic workflows nearly every day. Throughout GitHub, teams have been using agentic workflows to create custom tools for themselves in minutes, replacing chores with intelligence or paving the way for humans to get work done by assembling the right information, in the right place, at the right time. A new world of possibilities is opening for teams and enterprises to keep their repositories healthy, navigable, and high-quality.

Let’s talk guardrails and control

Designing for safety and control is non-negotiable. GitHub Agentic Workflows implements a defense-in-depth security architecture that protects against unintended behaviors and prompt-injection attacks.

Workflows run with read-only permissions by default. Write operations require explicit approval through safe outputs, which map to pre-approved, reviewable GitHub operations such as creating a pull request or adding a comment to an issue. Sandboxed execution, tool allowlisting, and network isolation help ensure that coding agents operate within controlled boundaries.

Guardrails like these make it practical to run agents continuously, not just as one-off experiments. See our security architecture for more details.

One alternative approach to agentic repository automation is to run coding agent CLIs, such as Copilot or Claude, directly inside a standard GitHub Actions YAML workflow. This approach often grants these agents more permission than is required for a specific task. In contrast, GitHub Agentic Workflows run coding agents with read-only access by default and rely on safe outputs for GitHub operations, providing tighter constraints, clearer review points, and stronger overall control.

What you can build with GitHub Agentic Workflows

If you’re looking for further inspiration Peli’s Agent Factory is a guided tour through a wide range of workflows, with practical patterns you can adapt, remix, and standardize across repos.

Note

A useful mental model: if repetitive work in a repository can be described in words, it might be a good fit for an agentic workflow.

If you’re looking for design patterns, check out ChatOps, DailyOps, DataOps, IssueOps, ProjectOps, MultiRepoOps, and Orchestration.

Uses for agent-assisted repository automation often depend on particular repos and development priorities. Your team’s approach to software development will differ from those of other teams. It pays to be imaginative about how you can use agentic automation to augment your team for your repositories for your goals.

Practical guidance for teams

Agentic workflows bring a shift in thinking. They work best when you focus on goals and desired outputs rather than perfect prompts. You provide clarity on what success looks like, and allow the workflow to explore how to achieve it. Some boundaries are built into agentic workflows by default, and others are ones you explicitly define. This means the agent can explore and reason, but its conclusions always stay within safe, intentional limits.

You will find that your workflows can range from very general (“Improve the software”) to very specific (“Check that all technical documentation and error messages for this educational software are written in a style suitable for an audience of age 10 or above”). You can choose the level of specificity that’s appropriate for your team.

GitHub Agentic Workflows use coding agents at runtime, which incur billing costs. When using Copilot with default settings, each workflow run typically incurs two premium requests: one for the agentic work and one for a guardrail check through safe outputs. The models used can be configured to help manage these costs. Today, automated uses of Copilot are associated with a user account. For other coding agents, refer to our documentation for details. Here are a few more tips to help teams get value quickly:

• Start with low-risk outputs such as comments, drafts, or reports before enabling pull request creation.
• For coding, start with goal-oriented improvements such as routine refactoring, test coverage, or code simplification rather than feature work.
• For reports, use instructions that are specific about what “good” looks like, including format, tone, links, and when to stop.
• Agentic workflows create an agent-only, sub-loop that’s able to be autonomous because agents are acting under defined terms. But it’s important that humans stay in the broader loop of forward progress in the repository, through reports, issues, and pull requests. With GitHub Agentic Workflows, pull requests are never merged automatically, and humans must always review and approve.
• Treat the workflow Markdown as code. Review changes, keep it small, and evolve it intentionally.

Continuous AI works best if you use it in conjunction with CI/CD. Don’t use agentic workflows as a replacement for GitHub Actions YAML workflows for CI/CD. This approach extends continuous automation to more subjective, repetitive tasks that traditional CI/CD struggle to express.

Build the future of automation with us

GitHub Agentic Workflows are available now in technical preview and are a collaboration between GitHub, Microsoft Research, and Azure Core Upstream. We invite you to try them out and help us shape the future of repository automation.

• Documentation
• How they work
• Quick start guide
• Workflow gallery

We’d love for you to be involved! Join us (and tons of other awesome makers) in the #agentic-workflows channel of the GitHub Next Discord or comment below. We look forward to seeing what you build with GitHub Agentic Workflows. Happy automating!

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Try GitHub Agentic Workflows in a repo today! Install gh-aw, add a starter workflow or create one using AI, and run it. Then, share what you build (and what you want next) below 👇

[kaihendry]

So it created an issue, so what do I do next? kaihendry/skills#3 close it?

[pelikhan]

You can use expires: 1d to make it auto close after a day, or use close-older-issues: true to have the latest issue close the previous one.

[pelikhan]

https://github.github.com/gh-aw/guides/ephemerals/

[supervoidcoder]

I wish it could use a free model so it wouldn’t use up a whole premium request (I’m not sure if it does, but I see it uses Claude sonnet 4.5 which usually takes up 1x request, so correct me if I’m wrong) for repetitive small tasks. For example the CI Doctor, we run tests on every commit. Would the only solution be to just make tests run weekly?

Also, I wish copilot agent could run on windows runners too. This way stuff like the cli test tool could run on my projects like win-witr and other cli tools which are very often Windows only since I know Windows best.