This course focuses on developing production-ready agentic AI systems that operate within GitHub-driven software development workflows. Learners explore agent architecture, tool integration, memory and execution management, evaluation techniques, multi-agent coordination, and governance practices using GitHub as the system of record and control plane.
Prerequisites for GitHub Agentic AI Developer Training
- Software Development Lifecycle (SDLC) Fundamentals
- GitHub Workflows and Repository Management
- GitHub Copilot Fundamentals
- AI Coding Agents
- Model Context Protocol (MCP) Fundamentals
- Agent Customization Fundamentals
What Will You Learn
- Prepare Agent Architectures for the SDLC
- Configure GitHub Agent Workflows
- Implement Tool Use and Environment Interaction
- Integrate Model Context Protocol (MCP) Servers
- Manage Agent Memory, State, and Execution
- Configure Custom AI Agents
- Evaluate and Improve Agent Performance
- Analyze Agent Errors and Outputs
- Orchestrate Multi-Agent Workflows
- Implement AI Guardrails and Human-in-the-Loop Controls
- Govern AI Agents with GitHub Controls
- Build Production-Ready Agentic AI Solutions on GitHub
Tools and Technologies Covered
- GitHub Copilot
- GitHub Models
- GitHub Actions
- GitHub Codespaces
- GitHub MCP Server
- Model Context Protocol (MCP)
- GitHub Agent Mode
- GitHub Repository Rules
- GitHub Pull Requests
- GitHub Issues
- GitHub Security Features
- Visual Studio Code
Who Should Do This Course
- Software Developers
- AI Application Developers
- GitHub Copilot Users
- Full-Stack Developers
- Backend Developers
- DevOps Engineers
- Platform Engineers
- AI Engineers
- Machine Learning Engineers
- Solutions Architects
- Technical Leads
- Developers Building AI Agents
