GitHub Agentic Workflows

Wake up to ready-to-review repository improvements-automated triage, CI insights, docs updates, and test enhancements from simple markdown workflows.

GitHub Agentic Workflows deliver this: repository automation, running the coding agents you know and love, in GitHub Actions, with strong guardrails and security-first design principles.

Use GitHub Copilot, Claude by Anthropic, Gemini from Google or OpenAI Codex for event-triggered and scheduled jobs to improve your repository. GitHub Agentic Workflows augment your existing, deterministic CI/CD with Continuous AI capabilities.

Developed by GitHub and Microsoft, workflows run with added guardrails, using safe outputs and sandboxed execution to help keep your repository safe.

ⓘ Note: GitHub Agentic Workflows is in early development and may change significantly. Using agentic workflows requires careful attention to security considerations and careful human supervision, and even then things can still go wrong. Use it with caution, and at your own risk.

AI agents can be manipulated by prompt injection, malicious repository content, or compromised tools. GitHub Agentic Workflows uses layered controls to keep each run contained: sandboxing limits where code can execute, scoped permissions limit what it can request, and gated outputs ensure only approved actions reach GitHub.

flowchart LR
    INPUT["Repository + Prompt Input"] --> TOKENS["Read-only Token"]
    TOKENS --> SECRETS["No Secrets in Agent"]
    SECRETS --> SANDBOX["Sandbox + Network Firewall"]
    SANDBOX --> SAFE["Safe Outputs Gate"]
    SAFE --> DETECT["Threat Detection Scan"]
    DETECT --> APPLY["Scoped Write Job"]

See the Security Architecture for a full breakdown of the layered defense-in-depth model.

Cost control starts with visibility. Use gh aw logs and gh aw audit to find runs consuming the most time, tokens, and AI Credits (AIC), then tighten prompts, triggers, and model choices before spend drifts upward.

max-ai-credits gives each run a hard budget, while OpenTelemetry exports traces and token data to OTLP backends for dashboards, alerting, and cost analysis. For optimization over time, compare cost with outcomes so lower spend still produces useful accepted results.

Here's a simple workflow that runs daily to create an upbeat status report:

---
on:
  schedule: daily

permissions:
  contents: read
  issues: read
  pull-requests: read

safe-outputs:
  create-issue:
    title-prefix: "[team-status] "
    labels: [report, daily-status]
    close-older-issues: true
---

## Daily Issues Report

Create an upbeat daily status report for the team as a GitHub issue.

## What to 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

The gh aw cli hardens this to a traditional GitHub Actions Workflow (.lock.yml) that runs an AI coding agent (Copilot CLI, Claude Code, Codex, ...) in a containerized environment on a schedule or manually. The AI coding agent reads your repository context, analyzes issues, generates visualizations, and creates reports. All defined in natural language rather than complex code.

Install the extension, add a sample workflow, and trigger your first run - all from the command line in minutes.

Create custom agentic workflows directly from the GitHub web interface using natural language.