What is Agentic Devops

Agentic-DevOps is a paradigm introduced at the Microsoft Build conference 2025, emphasizing the integration of AI-powered tools into DevOps practices. It is not a specific product but a framework accessible to all technology stacks and tools.

Use cases

Use cases include automated pull request reviews to ensure code quality, analysis of technical debt to maintain code health, and incident response automation to quickly address and resolve operational issues.

How to use

To use Agentic-DevOps, set up a Python virtual environment, clone the repository, install the necessary dependencies, and configure environment variables for the OpenAI API and GitHub tokens. Follow the installation and configuration steps outlined in the README.

Key features

Key features include the PR Review Agent for automated security analysis and documentation review, the Tech Debt Agent for dependency analysis and code complexity scanning, and the Incident Response Agent for automated incident diagnosis and remediation.

Where to use

Agentic-DevOps can be utilized in software development environments, particularly in teams that focus on continuous integration and delivery, as well as in organizations looking to enhance their DevOps practices with AI capabilities.

Agentic DevOps

The concept of Agentic DevOps got introduced in Microsoft Build conference 2025. Agentic DevOps is a paradigm, not a product. It’s accessible to all, regardless of your tech stack or tooling. Use AI-powered tools like Cursor, Windsurf, Claude Code, Continue, Cline, or open-source models like Llama 4, Mistral, DeepSeek, Qwen. Deploy on-prem with vLLM, SGLang, or in the cloud with AWS, GCP, Azure.

Agentic DevOps with DSPy + MCP

A comprehensive demonstration of Agentic DevOps using DSPy and Model Context Protocol (MCP). The convep

This suite includes three specialized agents and a combined demo showcasing automated PR reviews, tech debt analysis, and incident response.

🎯 Features

• PR Review Agent: Automated security analysis, documentation review, and impact assessment
• Tech Debt Agent: Dependency analysis, code complexity scanning, and test coverage reporting
• Incident Response Agent: Automated incident diagnosis, remediation, and reporting

📋 Prerequisites

• Python 3.8+
• Node.js 14+
• OpenAI API key
• GitHub personal access token
• Mac OS X or Linux (Windows support may vary)

🚀 Installation

1. Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

2. Clone the repository:

git clone https://github.com/superagenticai/agentic-devops.git
cd agentic-devops

3. Install Python dependencies:

pip install "agenspy[mcp]" dspy openai

4. Install Node.js dependencies:

npm install -g @modelcontextprotocol/server-github

⚙️ Configuration

1. Set up environment variables:

# For macOS/Linux
export OPENAI_API_KEY="your-openai-key"
export GITHUB_TOKEN="your-github-token"

# For Windows PowerShell
$env:OPENAI_API_KEY="your-openai-key"
$env:GITHUB_TOKEN="your-github-token"

Alternatively, create a .env file:

OPENAI_API_KEY=your-openai-key
GITHUB_TOKEN=your-github-token

🖥️ Running Individual Demos

PR Review Agent

python pr_review_agent.py

This will analyze a sample PR and provide:

• Security analysis
• Documentation review
• Implementation suggestions
• Impact assessment

Tech Debt Agent

python tech_debt_agent.py

This will scan a repository for:

• Deprecated dependencies
• Complex code modules
• Test coverage gaps
• Refactoring opportunities

Incident Response Agent

python incident_response_agent.py

This will simulate incident handling with:

• Automated diagnostics
• Remediation attempts
• Incident reporting
• On-call notifications

🎮 Running the Complete Demo

python agentic_devops_demo.py

This will run all three agents in sequence, demonstrating:

1. PR review workflow
2. Technical debt analysis
3. Incident response simulation

📁 Project Structure

agentic-devops/
├── pr_review_agent.py      # PR review automation
├── tech_debt_agent.py      # Technical debt analysis
├── incident_response_agent.py  # Incident response automation
└── README.md

🔑 API Keys and Permissions

OpenAI API Key

1. Visit OpenAI API
2. Create an account or log in
3. Navigate to API Keys section
4. Create a new secret key
5. Copy and set as OPENAI_API_KEY

GitHub Token

1. Visit GitHub Settings > Developer Settings
2. Generate new token (classic)
3. Select scopes:
   • repo (full access)
   • read:org
   • workflow
4. Copy and set as GITHUB_TOKEN

🤝 Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

⚠️ Common Issues

1. MCP Server Connection Failed

   # Check if server is running
   ps aux | grep mcp
   # Restart server
   npx @modelcontextprotocol/server-github
   

2. API Key Issues

   # Verify environment variables
   echo $OPENAI_API_KEY
   echo $GITHUB_TOKEN
   

3. Dependencies Missing

   # Reinstall dependencies
   pip install -r requirements.txt
   npm install -g @modelcontextprotocol/server-github
   

📝 License

MIT License - feel free to use and modify for your needs.

🙋‍♂️ Support

For issues and questions:

1. Check the Issues section
2. Create a new issue with detailed information