How It Works
GitHub Agentic Workflows enable AI agents to perform complex, multi-step tasks automatically with your team. Built on GitHub Actions, they use Claude Code, GitHub Copilot, or Codex to interpret natural language instructions and enable Continuous AI — systematic, automated application of AI to software collaboration. Below, we explore the core concepts that make this possible.
Workflow Structure
Section titled “Workflow Structure”Each workflow contains YAML frontmatter (triggers, permissions, tools) and markdown instructions (natural language tasks). This declarative structure enables reliable, secure agentic programming by sandboxing AI capabilities and triggering at the right moments.
---on: ...permissions: ...tools: ...---# Natural Language InstructionsAnalyze this issue and provide helpful triage comments...See Workflow Structure and Frontmatter for further configuration details.
AI Engines
Section titled “AI Engines”Workflows support GitHub Copilot (default), Claude Code, and Codex. Each engine interprets natural language instructions and executes them using configured tools and permissions. See AI Engines for details.
Tools and MCPs
Section titled “Tools and MCPs”Workflows use tools through the Model Context Protocol (MCP) for GitHub operations, external APIs, file operations, and custom integrations. Learn more in Tools and MCPs.
Agentic vs. Traditional Workflows
Section titled “Agentic vs. Traditional Workflows”Traditional GitHub Actions execute pre-programmed steps. Agentic workflows use AI to understand context, make decisions, use tools, and generate content by interpreting natural language instructions flexibly. Unlike traditional workflows, agentic workflows combine both deterministic GitHub Actions steps and flexible AI-driven instructions for context-aware automation.
Security Design
Section titled “Security Design”Agentic workflows run with minimal permissions (no write access by default), use tool allowlists to prevent unexpected actions, and process outputs through a safety layer before applying changes. Critical actions can require human approval. For more details, see Security Guide.
Compilation
Section titled “Compilation”Compile workflow .md files with gh aw compile to generate .lock.yml files containing the actual GitHub Actions workflow with security hardening and job orchestration. Commit both files — .md is the source of truth for editing, .lock.yml is the compiled output.
Continuous AI Patterns
Section titled “Continuous AI Patterns”Enable Continuous AI patterns: keep documentation current, incrementally improve code quality, intelligently triage issues and PRs, stay current with research, and automate code review and standards enforcement.
Best Practices
Section titled “Best Practices”Start simple and iterate. Write clear, specific instructions. Test with gh aw compile --watch and gh aw run. Monitor costs with gh aw logs. Review AI-generated content before merging. Use safe outputs processing for controlled creation of issues, comments, and PRs.
Next Steps
Section titled “Next Steps”Explore Workflow Structure, Frontmatter, Tools, Security Guide, and VS Code Integration to build sophisticated workflows that understand context, make intelligent decisions, and take meaningful actions while maintaining GitHub Actions reliability.