GH-600 Developing in Agentic AI Systems Exam Prep

Master GitHub agent architecture, MCP, memory, evaluation, multi-agent orchestration, guardrails and exam readiness.
What you'll learn

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Understand agent architecture and integrate AI agents into modern SDLC workflows.
Configure agent tools, MCP servers, permissions, execution environments, memory, and state.
Evaluate, troubleshoot, and tune agent behavior using logs, traces, artifacts, and evaluation signals.
Design multi-agent workflows and implement guardrails, human oversight, security, and accountability.
Prepare for the GH-600: Developing in Agentic AI Systems exam with quizzes, role plays, and practice exam questions.
Requirements

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Basic understanding of software development and the SDLC.
Familiarity with GitHub, repositories, branches, and pull requests is helpful.
Basic knowledge of GitHub Copilot or AI agents is recommended but not required.
No advanced AI or machine learning knowledge is required.
Description

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Prepare for theGH-600: Developing in Agentic AI Systems Certification Exam and master the core concepts required to design, operate, evaluate, and govern modern AI agents.
This comprehensive course takes you through the completeagentic AI development lifecycle, covering agent architecture, SDLC integration, tool use, environment interaction, memory and state management, evaluation, multi-agent orchestration, and guardrails.
You will learn how agents work inside modern software development workflows, how they use tools and environments, how they maintain context and memory, how multiple agents coordinate with each other, and how organizations can safely control agent autonomy through security, approvals, and human oversight.
Unlike basic AI agent tutorials, this course focuses onpractical concepts aligned with the GH-600 certification objectives, including
Agent Architecture and SDLC Integration
Agent Tools, Permissions, and Environment Interaction
Model Context Protocol (MCP) and Tool Integration
Short-Term, Long-Term, and External Memory
Agent State, Context, and Execution Management
Evaluation, Error Analysis, and Agent Tuning
Expected Outcomes and Operational Constraints
Multi-Agent Orchestration and Coordination
Sequential, Parallel, and Orchestrator-Based Workflows
Agent Delegation, Specialization, and Conflict Management
Guardrails, Human-in-the-Loop, and Approval Workflows
Operational, Security, and Compliance Risk Management
Least-Privilege Access and Safe Agent Autonomy
Accountability, Traceability, and Responsible Agent Operations
Through38 structured theory lectures, section-wise quizzes, role-play activities, practice exam questions, and exam-focused explanations, you will build both theconfidence to prepare for the GH-600 exam and the practical understanding needed to work with agentic AI systems in real-world development environments.
Whether you are a software developer, AI engineer, DevOps engineer, platform engineer, architect, GitHub Copilot user, or technology professional exploring agentic AI - this course will help you understand how to build and managereliable, secure, and scalable AI agent systems.
Who this course is for

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Developers preparing for the GH-600 certification exam.
GitHub Copilot and AI agent developers.
Software engineers working with agentic AI systems.
DevOps, platform, and cloud engineers integrating agents into development workflows.
Architects and technical professionals interested in secure and reliable multi-agent systems.
Anyone who wants to understand how AI agents operate inside GitHub-based software development workflows.
Homepage

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