Course GH-100T00-A: GitHub fundamentals - Administration basics and product features
Duration: 1 Day
In this course, you'll learn the basics of GitHub and gain a better understanding of its fundamental features with a hands-on exercise all within a GitHub repository. You'll learn best practices for building, hosting, and maintaining a secure repository on GitHub.
This course in intended for students who want to understand and GitHub best practices. You will understand the fundamental features of GitHub, learn about repository management, gain an understanding of the GitHub flow, including branches, commits, and pull requests. Additionally, you will explore the collaborative features of GitHub by reviewing issues and discussions and be able to manage your GitHub notifications and subscriptions.
Foundations of Agentic AI in GitHub
Learn how AI coding agents are transforming software development by planning, acting, and improving within GitHub workflows.
- Define agentic AI in the SDLC
- Explain the agent lifecycle - plan, act, evaluate
- Describe GitHub as the system of record and control plane
- Identify responsibilities, risks, anti-patterns, and traceability needs
- Apply the contributor model to agent-generated work
Designing Agent Architecture and SDLC Integration
Learn how agentic systems use GitHub workflows to build software safely.
- Map agent responsibilities to the SDLC
- Define inputs, outputs, and success criteria
- Separate planning, reasoning, and execution
- Examples of implementing PR governance with templates, checks, CODEOWNERS, rules, and environment gates
- Build reliable workflows - outputs, contexts, triggers, and cross-job handoffs
- Control and operate agents - observability, tools, MCP, secrets, hooks, and reliability
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.
- How agents interact with GitHub APIs and workflows
- Model Context Protocol (MCP) servers, registries, and allow lists
- Execution context and boundaries
- Agent execution limits and protections
Multi-Agent Systems and Orchestration
Learn how to design reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms.
- Define multi-agent responsibilities in the SDLC
- Orchestrate agents using GitHub workflows
- Isolate execution - branches, workflows, permissions, and concurrency
- Detect and resolve conflicts using GitHub-native arbitration
- Make the system observable - attribution, evidence, and handoffs
- Operate reliably at scale - diagnose failures and recover safely
Memory, State, and Evaluation
Learn how to manage agent memory and state, persist progress across environments, and evaluate agent behavior using clear success signals.
- Implement agent memory strategies
- Persist agent state and manage context drift
- Ensure continuity of agent memory and state across tools and environments
- Define evaluation signals and enforce quality gates
- Analyze agent failures and improve behavior
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.
- Define risk-based autonomy and action boundaries
- Enforce governance with GitHub controls
- Design human-in-the-loop workflows
- Control agent capabilities using least privilege
- Make actions observable, traceable, and auditable
- Maintain governance and operational reliability