Developing in Agentic AI Systems

• 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
• Design reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms
• Learn how to manage agent memory and state, persist progress across environments, and evaluate agent behavior using clear success signals
• Develop secure and compliant agent governance using GitHub-native controls, human-in-the-loop approvals, and least-privilege access

Learners should have subject matter expertise in operating, integrating, supervising, and governing AI agents inside production-grade SDLC workflows and development environments, ensuring reliability, safety, and velocity using GitHub as the system of record and control plane. Learners work closely with architects, platform engineers, DevOps engineers, application developers, product managers, and security engineers to develop, deploy, operate, and manage agents that operate within the GitHub platform. Learners should have experience with the software development lifecycle (SDLC), workflows in GitHub and controls, and code quality, security, and review practices. You should also have experience with coding agents including GitHub Copilot, MCP servers and agent customization such as custom instructions, custom agents, tools, and Copilot setup Responsibilities for this role include:

• Operating agent workflows inside the SDLC
• Supervising autonomous behavior with GitHub controls
• Evaluating and tuning agent outputs using scans and artifacts
• Configuring custom agents
• Coordinating multi-agent execution safely

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

• Developing in Agentic AI Systems Part 1 of 2
  • Foundations of Agentic AI in GitHub
  • Designing Agent Architecture and SDLC Integration
  • Tooling, MCP, and Agent Execution Environments
• Developing in agentic AI systems part 2 of 2
  • Multi-Agent Systems and Orchestration
  • Memory, State, and Evaluation
  • Governance, guardrails, and operations