Develop, deploy, and manage AI agents within GitHub software development workflows.

Agentic AI extends software development by enabling AI agents to plan tasks, execute actions, interact with development tools, and collaborate throughout the software development lifecycle (SDLC).

This course provides the practical knowledge required to design agent architectures, integrate AI agents into GitHub workflows, configure execution environments, and apply governance controls for production deployments.

  • Why get trained: Learn how to design agent architectures, configure Model Context Protocol (MCP) servers, integrate AI agents with GitHub workflows, manage agent memory and execution, coordinate multi-agent systems, and implement governance, security, and observability throughout the SDLC.
  • Why it matters: AI agents are becoming active participants in software engineering, assisting with development, testing, reviews, automation, and operational tasks. Development teams need engineers who understand how to supervise agent behaviour, enforce governance controls, evaluate outputs, and integrate autonomous workflows into existing engineering practices safely and reliably.
  • Who should attend: Software Developers, AI Engineers, DevOps Engineers, Platform Engineers, Solution Architects, GitHub administrators, Technical Leads, and professionals preparing for the GitHub Certified: Agentic AI Developer certification.

Develop the technical knowledge required to design, supervise, and operate agentic AI systems that integrate securely into modern GitHub development environments. HRD Corp Claimable.

Overview

This course is designed to build practical skills in developing, deploying, and managing agentic AI systems within GitHub-based software development workflows. 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.

Skills Covered

In this learning path, you’ll:

  • 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

Prerequisites

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

Target Audience

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

Course Curriculum

Module 1: Foundations of Agentic AI in GitHub

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

Module 2: Designing Agent Architecture and SDLC Integration

  • Learn how agentic systems use GitHub workflows to build software safely

Module 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

Module 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

Module 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

Module 6: Governance, Guardrails, and Operations

  • Learn how to design secure and compliant agent governance using GitHub-native controls, human-in-the-loop approvals, and least-privilege access
  • Understand operational safeguards to improve reliability, accountability, and recovery

Dates & Locations

Let’s make it work for you

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Speak to one of our learning experts today.

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August 14, 2026 - August 14, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 1200

August 14, 2026 - August 14, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 1200

October 2, 2026 - October 2, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 1200

October 2, 2026 - October 2, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 1200

November 13, 2026 - November 13, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 1200

November 13, 2026 - November 13, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 1200
Trainocate exam and cert

Exam & Certification

GitHub Certified: Agentic AI Developer

Demonstrate deep expertise in deploying, operating, integrating, and governing AI agents in production SDLC workflows, ensuring reliability, safety, and speed with GitHub as the control plane.

GitHub Certified: Agentic AI Developer, is a new, role-based certification focused on how developers and teams operate, supervise, and integrate AI agents across the software development lifecycle (SDLC).

  • Level: Intermediate
  • Product: GitHub
  • Role: AI Engineer, App Maker, Data Engineer, Developer, DevOps Engineer, Solution Architect
  • Subject: Application development, Artificial intelligence

Training & Certification Guide

You will have 120 minutes to complete this assessment.

Exam policy

This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

If you fail a certification exam, don’t worry. You can retake it 24 hours after the first attempt. For subsequent retakes, the amount of time varies. For full details, visit: Exam retake policy.

Assessed on this exam
  • Domain 1: Prepare agent architecture and SDLC processes (15–20%)
  • Domain 2: Implement Tool Use and Environment Interaction (20–25%)
  • Domain 3: Manage Memory, State, and Execution (10–15%)
  • Domain 4: Perform Evaluation, Error Analysis, and Tuning (15–20%)
  • Domain 5: Orchestrate Multi-Agent Coordination (15–20%)
  • Domain 6: Implement Guardrails and Accountability (10–15%)

You 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.

Your 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

You 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.

You 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 steps.

The exam is structured around six critical operational pillars: 
  • Agent Architecture & SDLC Processes (15–20%): Designing agent lifecycles and configuring autonomous branch creation or pull requests.
  • Tool Use & Environment Interaction (20–25%): Connecting agents to code repositories, CI/CD pipelines, and handling environment constraints.
  • Memory, State, & Execution (10–15%): Managing context windows, execution states, and data persistence across complex tasks.
  • Evaluation, Error Analysis, & Tuning (15–20%): Defining quantitative evaluation signals and implementing automated scanning tools.
  • Multi-Agent Coordination (15–20%): Orchestrating multiple independent agents working together without conflicting outputs.
  • Guardrails & Accountability (10–15%): Establishing human-in-the-loop triggers and safety constraints to manage agent autonomy.

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