Author name: ITMAITY

Open collaboration runs on trust. For a long time, that trust was protected by a natural, if imperfect filter: friction. If you were on Usenet in 1993, you’ll remember that every September a flood of new university students would arrive online, unfamiliar with the norms, and the community would patiently onboard them. Then mainstream dial-up ISPs became popular and a continuous influx of new users came online. It became the September that never ended. Today, open source is experiencing its own Eternal September. This time, it’s not just new users. It’s the sheer volume of contributions. When the cost to contribute drops In the era of mailing lists contributing to open source required real effort. You had to subscribe, lurk, understand the culture, format a patch correctly, and explain why it mattered. The effort didn’t guarantee quality, but it filtered for engagement. Most contributions came from someone who had genuinely engaged with the project. It also excluded people. The barrier to entry was high. Many projects worked hard to lower it in order to make open source more welcoming. A major shift came with the pull request. Hosting projects on GitHub, using pull requests, and labeling “Good First Issues” reduced the friction needed to contribute. Communities grew and contributions became more accessible. That was a good thing. But friction is a balancing act. Too much keeps people and their ideas out, too little friction can strain the trust open source depends on. Today, a pull request can be generated in seconds. Generative AI makes it easy for people to produce code, issues, or security reports at scale. The cost to create has dropped but the cost to review has not. It’s worth saying: most contributors are acting in good faith. Many want to help projects they care about. Others are motivated by learning, visibility, or the career benefits of contributing to widely used open source. Those incentives aren’t new and they aren’t wrong. The challenge is what happens when low-quality contributions arrive at scale. When volume accelerates faster than review capacity, even well-intentioned submissions can overwhelm maintainers. And when that happens, trust, the foundation of open collaboration, starts to strain. The new scale of noise It is tempting to frame “low-quality contributions” or “AI slop” contributions as a unique recent phenomenon. It isn’t. Maintainers have always dealt with noisy inbound. The Linux kernel operates under a “web of trust” philosophy and formalized its SubmittingPatches guide and introduced the Developer Certificate of Origin (DCO) in 2004 for a reason. Mozilla and GNOME built formal triage systems around the reality that most incoming bug reports needed filtering before maintainers invested deeper time. Automated scanners: Long before GenAI, maintainers dealt with waves of automated security and code quality reports from commercial and open source scanning tools. The question from maintainers has often been the same: “Are you really trying to help me, or just help yourself?“ Just because a tool—whether a static analyzer or an LLM—makes it easy to generate a report or a fix, it doesn’t mean that contribution is valuable to the project. The ease of creation often adds a burden to the maintainer because there is an imbalance of benefit. The contributor maybe gets the credit (or the CVE, or the visibility), while the maintainer gets the maintenance burden. Maintainers are feeling that directly. For example: curl ended its bug bounty program after AI-generated security reports exploded, each taking hours to validate. Projects like Ghostty are moving to invitation-only contribution models, requiring discussion before accepting code contributions. Multiple projects are adopting explicit rules about AI-generated contributions. These are rational responses to an imbalance. What we’re doing at GitHub At GitHub, we aren’t just watching this happen. Maintainer sustainability is foundational to open source, and foundational to us. As the home of open source, we have a responsibility to help you manage what comes through the door. We are approaching this from multiple angles: shipping immediate relief now, while building toward longer-term, systemic improvements. Some of this is about tooling. Some is about creating clearer signals so maintainers can decide where to spend their limited time. Features we’ve already shipped Pinned comments on issues: You can now pin a comment to the top of an issue from the comment menu. Banners to reduce comment noise: Experience fewer unnecessary notifications with a banner that encourages people to react or subscribe instead of leaving noise like “+1” or “same here.” Pull request performance improvements: Pull request diffs have been optimized for greater responsiveness and large pull requests in the new files changed experience respond up to 67% faster. Faster issue navigation: Easier bug triage thanks to significantly improved speeds when browsing and navigating issues as a maintainer. Temporary interaction limits: You can temporarily enforce a period of limited activity for certain users on a public repository. These improvements focus on reducing review overhead. Features we’ll be shipping soon Repo-level pull request controls: Gives maintainers the option to limit pull request creation to collaborators or disable pull requests entirely. While the introduction of the pull request was fundamental to the growth of open source, maintainers should have the tools they need to manage their projects. Pull request deletion from the UI: Remove spam or abusive pull requests so repositories can stay more manageable. Exploring next steps We know that walls don’t build communities. As we explore next steps, our focus is on giving maintainers more control while helping protect what makes open source communities work. Some of the directions we’re exploring in consultation with maintainers include: Criteria-based gating: Requiring a linked issue before a pull request can be opened, or defining rules that contributions must meet before submission. Improved triage tools: Potentially leveraging automated triage to evaluate contributions against a project’s own guidelines (like CONTRIBUTING.md) and surface which pull requests should get your attention first. These tools are meant to support decision-making, not replace it. Maintainers should always remain in control. We are also aware of tradeoffs. Restrictions can disproportionately affect first-time contributors

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. 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. 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: Continuous triage: automatically summarize, label, and route new issues. Continuous documentation: keep READMEs and documentation aligned with code changes. Continuous code simplification: repeatedly identify code improvements and open pull requests for them. Continuous test improvement: assess test coverage and add high-value tests. Continuous quality hygiene: proactively investigate CI failures and propose targeted fixes. 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. A simple example: A daily repo report   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: Generate a workflow that creates a daily repo status report for a maintainer. Use the instructions at https://github.com/github/gh-aw/blob/main/create.md 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:  — on: schedule: daily permissions: contents: read issues: read pull-requests: read safe-outputs: create-issue: title-prefix: “[repo status] ” labels: [report] tools: github: — # Daily Repo Status Report Create a daily status report for maintainers. Include – Recent repository activity (issues, PRs, discussions, releases, code changes) – Progress tracking, goal reminders and highlights – Project status and recommendations – Actionable next steps for maintainers Keep it concise and link to the relevant issues/PRs. This file has two parts:  Frontmatter (YAML between — markers) for configuration  Markdown instructions that describe the job in natural language in natural language The Markdown is the intent, but the trigger, permissions, tools, and allowed outputs

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