RESEARCH PROTOTYPE

GitHub Agentic Workflows

What are Agentic Workflows?

Agentic workflows are AI-powered automation that can understand context, make decisions, and take meaningful actions—all from natural language instructions you write in markdown.

Unlike traditional automation with fixed if-then rules, agentic workflows use AI agents (like GitHub Copilot) to:

• Understand context: Read your repository, issues, and pull requests to grasp the current situation
• Make decisions: Choose appropriate actions based on the context, not just predefined conditions
• Adapt behavior: Respond flexibly to different scenarios without requiring explicit programming for each case

For example, instead of writing complex scripts to triage issues, you simply describe what you want: “Analyze this issue and ask for clarification if details are missing.” The AI agent reads the issue, understands what information is needed, and generates an appropriate response.

From this (natural language):

When an issue is opened, analyze it and ask for
clarification if important details are missing.

To automated action: The AI reads the issue, determines what’s missing, and posts a helpful comment requesting specific information—adapting its response to each unique issue.

This is “agentic” because the AI acts as an intelligent agent with agency to make context-aware decisions, rather than just executing predefined steps.

Part of the Continuous AI Initiative
Learn more about systematic, automated application of AI to software collaboration in the GitHub Next Agentic Workflows blog post.

Key Features

Natural Language Workflows

Write automation in markdown instead of complex YAML

AI-Powered Decision Making

Workflows that understand context and adapt to situations

GitHub Integration

Deep integration with issues, PRs, discussions, and repository management

Security First

Sandboxed execution with minimal permissions and safe output processing

Multiple AI Engines

Support for Claude, Codex, and custom AI processors

Continuous AI

Systematic, automated application of AI to software collaboration

Workflow Examples

ChatOps

command

Interactive automation triggered by slash commands like /review and /deploy

IssueOps

automated

Auto-triage and respond to issues as they’re created

LabelOps

automated

Workflows triggered by label changes on issues and PRs

DailyOps

scheduled

Small daily improvements that compound over time

Research & Planning

scheduled

Weekly research reports and automated team status updates

Triage & Analysis

automated

Intelligent issue triage and CI failure investigation

Coding & Development

automated

PR assistance and automated dependency updates

Quality & Testing

automated

Test coverage improvements and performance optimization