Course
digicode: GH600
Developing in Agentic AI Systems – Intensive Training (GH-600)
GH-600
Course facts
Download as PDF- Integrating AI agents into the software development lifecycle (SDLC) by defining agent tasks, inputs/outputs, and execution boundaries
- Designing and configuring agent architectures that separate planning, reasoning, and execution to improve reliability and control
- Implementing tool use and environment interactions by configuring agent tools, permissions, and MCP servers within development environments
- Designing reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms
- Learning how to manage agent memory and state, persisting progress across environments, and evaluating agent behavior using clear success signals
- Developing secure and compliant agent governance using GitHub-native controls, human-in-the-loop approvals, and least-privilege access
The course explores how to integrate AI agents into the software development lifecycle (SDLC), including designing agent architectures, configuring tools and environments, and managing agent memory, state, and execution. Students will learn how to evaluate and optimize agent performance, implement governance and guardrails, and coordinate multi-agent systems to ensure safe, reliable, and efficient outcomes. Through hands-on learning, participants will gain the skills needed to operate, supervise, and govern AI agents in production environments using GitHub as the control plane.
1 Foundations of Agentic AI in GitHub
Learn how AI coding agents are transforming software development by planning, acting, and improving within GitHub workflows.
2 Designing Agent Architecture and SDLC Integration
Learn how agentic systems use GitHub workflows to build software safely.
3 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.
4 Multi-Agent Systems and Orchestration
Learn how to design reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms.
5 Memory, State, and Evaluation
Learn how to manage agent memory and state, persist progress across environments, and evaluate agent behavior using clear success signals.
6 Governance, guardrails, and operations
This module covered how to design secure and compliant agent governance using GitHub-native controls, human-in-the-loop approvals, and least-privilege access. It also introduced operational safeguards to improve reliability, accountability, and recovery.
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