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Discover GitHub Agentic Workflows, now in technical preview. Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.
Imagine visiting your repository in the morning and feeling calm because you see:
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. In this post, we’ll explain 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.
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:
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.
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.
Let’s look at an agentic workflow which creates a daily status report for repository maintainers.
In practice, you will usually use AI assistance to create your workflows. The easiest way to do this is with an interactive coding agent. For example, with your favorite coding agent, you can enter this prompt:
The coding agent will interact with you to confirm your specific needs and intent, write the Markdown file, and check its validity. You can then review, refine, and validate the workflow before adding it to your repository.
This will create two files in .github/workflows:
daily-repo-status.md (the agentic workflow) daily-repo-status.lock.yml (the corresponding agentic workflow lock file, which is executed by GitHub Actions) The file daily-repo-status.md will look like this:
This file has two parts:
--- markers) for configuration The Markdown is the intent, but the trigger, permissions, tools, and allowed outputs are spelled out up front.
If you prefer, you can add the workflow to your repository manually:
daily-repo-status.md with the frontmatter and instructions.gh extension install github/gh-aw gh aw compileOnce you add this workflow to your repository, it will run automatically or you can trigger it manually using GitHub Actions. When the workflow runs, it creates a status report issue like this:
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.
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.
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:
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.
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.
We’d love for you to be involved! Share your thoughts in the Community discussion, or join us (and tons of other awesome makers) in the #agentic-workflows channel of the GitHub Next Discord. We look forward to seeing what you build with GitHub Agentic Workflows. Happy automating!
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).
GitHub Agentic Workflows are built with isolation, constrained outputs, and comprehensive logging. Learn how our threat model and security architecture help teams run agents safely in GitHub Actions.