Developing in Agentic AI Systems Part 1 of 2

  • Learning Path
  • 3 Modules

At a glance

Learn how to design, deploy, and manage agentic AI systems within the software development lifecycle.

In this learning path, you'll:

  • Integrate AI agents into the software development lifecycle (SDLC) by defining agent tasks, inputs/outputs, and execution boundaries
  • Design and configure agent architectures that separate planning, reasoning, and execution to improve reliability and control
  • Implement tool use and environment interactions by configuring agent tools, permissions, and MCP servers within development environments

Prerequisites

  • A GitHub account
  • Basic understanding of AI fundamentals
  • Basic understanding of repositories, branches, and pull requests
  • General knowledge of CI and CD concepts

Modules in this learning path

Foundations of Agentic AI in GitHub

Learn how AI coding agents are transforming software development by planning, acting, and improving within GitHub workflows.

Designing Agent Architecture and SDLC Integration

Learn how agentic systems use GitHub workflows to build software safely.

Tooling, MCP, and Agent Execution Environments

Learn how agents use tools, MCP, and GitHub workflows to execute tasks safely, with clear boundaries, security controls, and scalable automation.

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