Set Up GitHub Agentic Workflows in Actions

A terminal window showing the gh extension install github/gh-aw command and the verbatim changelog entry for GitHub Agentic Workflows public preview
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What this replaces

Automating a reasoning-based repo task, deciding whether an issue is a duplicate, summarizing why a CI run failed, drafting a documentation update, used to mean hand-writing bespoke Actions YAML and whatever custom code called an LLM API, with no standard security boundary around what that code could read, write, or execute inside CI. Every team building this kind of automation solved the sandboxing and output-validation problem themselves, if they solved it at all.

GitHub’s changelog, published 2026-06-11:

“GitHub Agentic Workflows is now in public preview. With agentic workflows, you can automate reasoning-based tasks like issue triage, CI failure analysis, and documentation updates by leveraging coding agents inside GitHub Actions.”

Workflows are authored as plain-language Markdown, not YAML directly:

“You define your automation using natural language Markdown files, and GitHub Agentic Workflows compiles them into standard Actions YAML.”

The security layer is the actual news here

Four separate safeguards ship with the preview: an integrity filter on what content an agent can access, read-only permissions by default, sandboxed execution inside an “Agent Workflow Firewall,” and a dedicated threat-detection job that scans an agent’s proposed changes before they apply. A “safe outputs” process validates what the agent produces before anything from it lands in the repo. None of that existed as a standard feature for hand-rolled agent-in-CI setups before this.

Structural Comparison Matrix

Operational AspectHand-rolled agent automation (before)GitHub Agentic Workflows (public preview)
Authoring formatActions YAML plus custom LLM-calling codeNatural-language Markdown, compiled to Actions YAML
Default permissionsWhatever the team’s own YAML grantedRead-only by default
Execution sandboxingWhatever the team built themselves, if anythingRuns inside the Agent Workflow Firewall
Output validationCustom, team-specific, if it exists at allA dedicated “safe outputs” process
Pre-apply reviewManual code review of generated changesA threat-detection job scans proposed changes first

Install the CLI extension and scaffold a workflow

GitHub Agentic Workflows ships as a gh CLI extension, not a built-in gh subcommand:

install the extension
gh extension install github/gh-aw

Running gh aw compile produces a .lock.yml file containing the standard Actions YAML the Markdown source compiled into, which is the file Actions actually runs, not the Markdown itself. From a repo’s root, the wizard scaffolds a first workflow interactively:

scaffold a starter workflow
gh aw add-wizard githubnext/agentics/daily-repo-status

The wizard’s reference argument uses an OWNER/REPO/WORKFLOW-NAME format, pulling a starter template, in this case a daily repo status check, from GitHub’s own githubnext/agentics collection of prebuilt workflows. It walks through checking repository prerequisites, choosing an AI engine (Copilot by default, or Claude Code, OpenAI Codex, Google Gemini, or Pi instead), and setting up whichever secret that engine needs.

No manual install step if your gh CLI is current

GitHub’s docs note that with gh CLI 2.90.0 or later, running any gh aw command prompts an automatic install of the extension if it isn’t present yet, so the explicit gh extension install step above is a fallback more than a hard requirement on a recent gh.

Where the workflow actually lives

The scaffolded workflow is a Markdown file at .github/workflows/<workflow-name>.md, for example .github/workflows/daily-repo-status.md. After editing that file’s frontmatter or body, recompile it so the change actually takes effect:

recompile after editing the Markdown source
gh aw compile

That command regenerates the matching .lock.yml file, the version Actions actually executes. Both files belong in the repo: the Markdown source, because it’s what a human edits, and the compiled .lock.yml, because Actions never compiles Markdown on its own at run time.

Would this help this site’s own workflows?

Not yet, in any concrete way worth claiming. This site’s .github/workflows/ci.yml runs deterministic build, lint, and test steps, not the kind of reasoning-based task, issue triage, CI failure summarization, this feature targets. A plausible future use here would be automating triage on reader-submitted issues or summarizing why a Lighthouse CI budget failed, but nothing in this repo uses agentic workflows today, and this preview’s public-preview status is reason enough to wait before wiring it into a pipeline that gates a real deploy.

Confirmed version

Sourced from GitHub’s changelog entry announcing the public preview, published 2026-06-11, and corroborated against GitHub’s Agentic Workflows quickstart for the install and scaffolding commands above. Browse more posts like this in the Dev Tools archive.

Frequently asked

Does GitHub Agentic Workflows lock me into GitHub Copilot?

No. Copilot is the default engine the setup wizard offers, but GitHub's own quickstart lists Claude Code, OpenAI Codex, Google Gemini, and Pi as other built-in engine options, each configured through its own secret (COPILOT_GITHUB_TOKEN, ANTHROPIC_API_KEY, OPENAI_API_KEY, or GEMINI_API_KEY) during setup.

Is this ready for production use?

GitHub's own changelog labels it public preview as of 2026-06-11, not general availability. Treat it the way you'd treat any preview feature: useful to pilot on a low-stakes workflow like issue triage, but not yet the kind of guarantee you'd want backing a release-blocking gate.

Do I edit the compiled .lock.yml file directly?

No, and this wasn't independently reproduced here beyond what GitHub's own docs describe. The Markdown file is the source of truth; running gh aw compile regenerates the .lock.yml Actions actually executes. Hand-editing the generated YAML directly would just get overwritten the next time the Markdown source is recompiled.

Emitted as FAQPage JSON-LD from the same frontmatter — one source, no duplicated prose.

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