GitHub Agenetic AI Developer GH-600

• Integrate AI agents into software development lifecycle workflows.
• Design agent architectures with clear planning, reasoning, and execution boundaries.
• Configure agent tools, permissions, MCP servers, and execution environments.
• Manage agent memory, state, context, and execution.
• Evaluate agent performance, analyze failures, and tune agent behavior.
• Orchestrate and manage multi-agent workflows.
• Implement guardrails, human-in-the-loop controls, least-privilege access, and accountability.

Part 1 of 2: Developing in Agentic AI Systems

Module 1: Foundations of Agentic AI in GitHub

• Define agentic AI in the software development lifecycle.
• Explain the agent lifecycle: plan, act, and evaluate.
• Describe GitHub as the system of record and control plane.
• Identify responsibilities, risks, anti-patterns, and traceability requirements.
• Apply the contributor model to agent-generated work.

Module 2: Designing Agent Architecture and SDLC Integration

• Map agent responsibilities to the software development lifecycle.
• Define inputs, outputs, and success criteria.
• Separate planning, reasoning, and execution.
• Implement pull request governance using templates, checks, CODEOWNERS, rules, and environment gates.
• Build reliable workflows using outputs, contexts, triggers, and cross-job handoffs.
• Control and operate agents using observability, tools, MCP, secrets, hooks, and reliability practices.

Module 3: Tooling, MCP, and Agent Execution Environments

• Understand how agents interact with GitHub APIs and workflows.
• Understand Model Context Protocol servers, registries, and allow lists.
• Configure execution contexts and boundaries.
• Understand agent execution limits and protections.
• Configure GitHub Actions and GitHub Agentic Workflows for agent execution.

Part 2 of 2: Developing in Agentic AI Systems

Module 4: Multi-Agent Systems and Orchestration

• Define multi-agent responsibilities in the software development lifecycle.
• Orchestrate agents using GitHub workflows.
• Isolate execution using branches, workflows, permissions, and concurrency.
• Detect and resolve conflicts using GitHub-native controls.
• Make multi-agent systems observable through attribution, evidence, and handoffs.
• Diagnose failures and implement recovery mechanisms.
• Build multi-agent workflows using GitHub Copilot CLI custom agents.

Module 5: Memory, State, and Evaluation

• 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 quality gates.
• Analyze agent failures and improve agent behavior.

Module 6: Governance, Guardrails, and Operations

• Define risk-based autonomy and action boundaries.
• Enforce governance using GitHub controls.
• Design human-in-the-loop workflows.
• Control agent capabilities using least-privilege principles.
• Make agent actions observable, traceable, and auditable.
• Maintain governance and operational reliability.

1 Day

Certification: GitHub Certified: Agentic AI Developer

Exam: GH-600: Developing in Agentic AI Systems

Duration: 120 minutes

• Automating repository updates with GitHub Agentic Workflows.
• Configuring agent tools and MCP servers.
• Defining execution contexts and boundaries.
• Configuring agent permissions and safe execution paths.
• Building multi-agent workflows using GitHub Copilot CLI custom agents.
• Managing agent memory and persistent state.
• Evaluating agent behavior and defining quality gates.
• Implementing governance and human-in-the-loop workflows.
• Applying least-privilege controls.
• Making agent actions observable, traceable, and auditable.

• AI Engineers.
• Developers working with agentic AI systems.
• DevOps Engineers.
• Solution Architects.
• Administrators working with GitHub agent workflows.
• Professionals responsible for operating, supervising, integrating, and governing AI agents within GitHub-based SDLC workflows.

• A GitHub account.
• Basic understanding of AI fundamentals.
• Basic understanding of repositories, branches, and pull requests.
• General knowledge of CI and CD concepts.
• Familiarity with GitHub Actions and agent workflows is recommended.
• Experience with GitHub Copilot, MCP servers, custom instructions, custom agents, and tools is relevant to the certification profile.