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What Are Agentic Workflows?

Already familiar with both GitHub Actions and AI agent execution environments?

Before skipping, confirm you already know both of these:

  • You can describe what an Actions workflow trigger does
  • You have worked with AI agent execution environments in a production or CI/CD context

If both apply, Skip to Install gh-aw.

📋 Before You Start

An Agentic Workflow is a plain-English task brief that an AI agent executes inside GitHub Actions. You write what you want — "summarize open issues and post a daily digest" — and the agent reads your repo, calls tools, reasons about the results, and posts the output automatically. The frontmatter is fully Actions-compatible — triggers, permissions, and runners all apply.

Animated GitHub Actions run showing four security jobs: activation validates the agent is authorized to run, agent runs with sandbox, firewall, and integrity filter enabled, detection scans for malicious code, and safe-outputs applies changes within guardrails

Why not just use a standard Actions workflow?

Three concrete differences a DevOps engineer will notice immediately:

  • Agent reasoning loop: Each run, the agent reads live repository context, decides what matters, and composes output that differs every time — no two runs are identical.
  • Natural-language task brief: You write what you want in plain English. No run: scripts, no fixed shell commands.
  • Dynamic tool use: The agent calls tools (read files, list issues, search code) based on what it discovers at runtime — not a predetermined sequence of steps hardcoded in YAML.

If you already write Actions YAML, the frontmatter stays the same (triggers, permissions, runners). And it is not one-or-the-other: agentic workflows can include custom jobs and deterministic steps alongside the AI agent — fixed data-fetch steps can run first, then the agent interprets and synthesizes the results.

Three things to know

Agentic workflow lifecycle: a Markdown file with YAML frontmatter and a task brief is compiled by gh aw compile into a lock.yml file, which GitHub Actions triggers, runs the AI agent that reads repository data and calls tools, and produces a structured output posted back to GitHub

  • What it is: A Markdown file (.md) with YAML frontmatter and a plain-language brief. gh aw compile converts it into a standard Actions workflow (.lock.yml) that runs the agent.
  • What it produces: A synthesized report or action the agent composes from live repository data — different every run based on what it finds.
  • Why it exists: Classic Actions handles deterministic CI/CD. Agentic workflows fill the gap for tasks that need judgment — or you can mix both in a single hybrid workflow.

Tip

Want to go deeper? Side Quest: Agentic Workflows Deep Dive covers exercises, example output, the two-file structure, and concept checks.

✅ Checkpoint

  • I can describe what an agentic workflow is in one sentence
  • I can explain one way an agentic workflow differs from a standard Actions workflow
  • I can identify the three parts: trigger → agent → safe output

Next: Install the gh-aw CLI Extension