GH-600 : Agentic AI Developer

• Duration: 1 day

Overview

This course develops practical skills for designing, developing, deploying, operating, and governing agentic AI systems within GitHub-based software development workflows.

Participants will explore how AI agents can be integrated into the Software Development Lifecycle (SDLC), including agent architecture, tool integration, execution environments, memory and state management, and agent customization.

The course also covers multi-agent orchestration, performance evaluation, governance, guardrails, human-in-the-loop controls, and least-privilege access. Through practical learning, participants will gain the knowledge required to operate, supervise, and govern AI agents in production-grade development environments, using GitHub as a system of record and control plane.

What You’ll Learn

• Integrate AI agents into the Software Development Lifecycle (SDLC) by defining tasks, inputs, outputs, and execution boundaries.
• Design agent architectures that separate planning, reasoning, and execution to improve reliability and control.
• Configure agent tools, permissions, environments, and MCP servers for secure agent execution.
• Design and coordinate reliable multi-agent systems using observable workflows and coordinated artifacts.
• Manage agent memory and state to persist progress across environments.
• Evaluate and optimize agent behavior using measurable success signals, scans, and artifacts.
• Implement secure and compliant agent governance and guardrails.
• Apply human-in-the-loop approvals and least-privilege access.
• Configure and manage custom agents within GitHub-based development workflows.
• Supervise autonomous agent behavior while maintaining security, reliability, and accountability.

Who Should Attend

This course is intended for professionals responsible for operating, integrating, supervising, and governing AI agents within production-grade SDLC workflows and development environments.

It is particularly suitable for:

• AI Developers and Engineers: Professionals developing and integrating AI agents into software development workflows.
• Software Architects: Professionals designing agent architectures and defining execution boundaries and system controls.
• Platform and DevOps Engineers: Individuals responsible for development environments, automation, CI/CD, and agent operations.
• Application Developers: Developers using coding agents and AI-assisted development tools within GitHub.
• Security Engineers: Professionals responsible for security controls, governance, permissions, and safe agent execution.
• Product Managers and Technical Leads: Professionals coordinating AI-enabled development workflows and evaluating agent outcomes.
• GitHub and DevSecOps Professionals: Individuals responsible for managing GitHub-based development processes, code quality, security, and review practices.

Key Responsibilities Covered

Participants will develop skills related to:

• Operating agent workflows within the SDLC.
• Supervising autonomous agent behavior using GitHub controls.
• Evaluating and tuning agent outputs using scans and artifacts.
• Configuring and managing custom agents.
• Working with GitHub Copilot, MCP servers, tools, and agent customization.

Prerequisites

• A GitHub account.
• Basic understanding of AI fundamentals.
• Basic understanding of repositories, branches, and pull requests.
• General knowledge of CI/CD concepts.
• Familiarity with software development lifecycle practices is recommended.
• Experience with GitHub workflows, code quality, security, and review practices is beneficial.

Curriculum

Module 01: Foundations of Agentic AI in GitHub

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

Topics include:

• Introduction to Agentic AI
• AI coding agents and software development
• Agent capabilities and workflows
• Planning, reasoning, and execution
• GitHub as a control plane for AI agents
• Integrating agents into the SDLC
• Defining agent tasks and execution boundaries
• GitHub Copilot and coding agents

Module 02: Designing Agent Architecture and SDLC Integration

Learn how agentic systems can use GitHub workflows to build and modify software safely and reliably.

Topics include:

• Agent architecture fundamentals
• Planning and reasoning workflows
• Execution boundaries
• Agent inputs and outputs
• Integrating agents into the SDLC
• GitHub workflows and controls
• Agent responsibilities and task definition
• Reliability and control mechanisms
• Safe agent-driven development

Module 03: Tooling, MCP, and Agent Execution Environments

Learn how AI agents use tools, Model Context Protocol (MCP), and development environments to execute tasks safely.

Topics include:

• Agent tools and capabilities
• Tool configuration and permissions
• Model Context Protocol (MCP)
• MCP servers
• Agent execution environments
• Environment configuration
• Security boundaries and access controls
• Least-privilege principles
• Safe and scalable agent automation

Module 04: Multi-Agent Systems and Orchestration

Learn how to design and coordinate reliable multi-agent systems using GitHub workflows, observable processes, and coordinated artifacts.

Topics include:

• Multi-agent system fundamentals
• Agent roles and responsibilities
• Agent coordination and orchestration
• GitHub-based agent workflows
• Coordinated artifacts
• Observable agent execution
• Managing dependencies between agents
• Failure handling and recovery mechanisms
• Designing reliable multi-agent workflows

Module 05: Memory, State, and Evaluation

Learn how to manage agent memory and state, preserve progress across environments, and evaluate agent behavior using measurable success signals.

Topics include:

• Agent memory fundamentals
• Managing agent state
• Persisting agent progress
• State across development environments
• Agent behavior evaluation
• Defining success signals
• Evaluating agent outputs
• Using scans and artifacts for evaluation
• Monitoring and optimizing agent performance

Module 06: Governance, Guardrails, and Operations

Learn how to implement secure and compliant governance for AI agents using GitHub-native controls, human oversight, and operational safeguards.

Topics include:

• Agent governance fundamentals
• Security and compliance considerations
• GitHub-native controls
• AI agent guardrails
• Human-in-the-loop approvals
• Least-privilege access
• Operational safeguards
• Agent accountability and traceability
• Reliability and recovery
• Supervising autonomous agent behavior
• Managing agents in production environments

Course Outcome

Upon successful completion of this course, participants will be able to:

• Explain the role of Agentic AI within modern software development workflows.
• Integrate AI agents into the SDLC with clearly defined tasks and execution boundaries.
• Design and configure agent architectures for reliable software development.
• Configure tools, permissions, MCP servers, and execution environments.
• Build and orchestrate multi-agent workflows using GitHub.
• Manage agent memory and state across development environments.
• Evaluate and optimize agent behavior using measurable success signals.
• Implement governance, guardrails, and human-in-the-loop controls.
• Apply least-privilege access and secure operational practices.
• Configure custom agents and supervise AI-assisted development workflows in production-grade environments.