How It Works

GitHub Agentic Workflows enable AI agents (autonomous AI systems that can make decisions) to perform complex, multi-step tasks automatically with your team. Built on GitHub Actions, they use GitHub Copilot (or experimental engines like Claude and Codex) to interpret natural language instructions and enable Continuous AI — systematic, automated application of AI to software collaboration.

Workflow Structure

Each workflow contains YAML frontmatter (the configuration section between --- markers) and markdown instructions. The frontmatter defines triggers, permissions, and tools, while the markdown contains natural language task descriptions. This declarative structure enables reliable, secure agentic programming by sandboxing AI capabilities and triggering at the right moments.

---
on: ...
permissions: ...
tools: ...
---
# Natural Language Instructions
Analyze this issue and provide helpful triage comments...

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.

Tools and MCPs

Workflows use tools through the Model Context Protocol (MCP) — a standardized protocol for connecting AI agents to external tools and services — for GitHub operations, external APIs, file operations, and custom integrations.

Agentic vs. Traditional Workflows

Traditional workflows execute pre-programmed steps with fixed if/then logic. They do exactly what you tell them, every time, in the same way.

Agentic workflows use AI to understand context, make decisions, and generate content by interpreting natural language instructions flexibly. They combine deterministic GitHub Actions infrastructure with AI-driven decision-making, adapting their behavior based on the specific situation they encounter.

Security Design

Agentic workflows implement a defense-in-depth security architecture that protects against prompt injection, rogue MCP servers, and malicious agents. The architecture operates across multiple layers: compilation-time validation, runtime isolation, permission separation, network controls, and output sanitization.

flowchart LR
    INPUT["📥 Input"] --> COMPILE["🔒 Compile"]
    COMPILE --> RUNTIME["⚙️ Runtime"]
    RUNTIME --> ISOLATION["🛡️ Isolation"]
    ISOLATION --> OUTPUT["📤 Output"]
    OUTPUT --> ACTIONS["✅ Actions"]

Workflows run with minimal permissions (no write access by default), use tool allowlists, and process outputs through a safety layer before applying changes. Critical actions can require human approval. For detailed security documentation, see the Security Architecture page.

Compilation

Use gh aw compile to generate .lock.yml files (compiled GitHub Actions workflow files) from workflow .md files. The .md file is the editable source of truth, while .lock.yml is the compiled GitHub Actions workflow with security hardening. Commit both files.

Continuous AI Patterns

Enable Continuous AI patterns like keeping documentation current, improving code quality incrementally, intelligently triaging issues and PRs, and automating code review.

Best Practices

Start simple and iterate with clear, specific instructions. Test workflows using gh aw compile --watch and gh aw run, monitor costs with gh aw logs, and review AI-generated content before merging. Use safe outputs for controlled creation of issues, comments, and PRs.