GitHub Agentic AI Developer Exam Course Prep

About this course

This GitHub Agentic AI Developer course is designed to help you understand how agentic AI systems are planned, built, managed, evaluated, and governed within modern software development workflows. The course covers essential concepts such as agent architecture, SDLC integration, tool use, environment interaction, memory, state management, execution control, error analysis, tuning, multi-agent coordination, guardrails, human oversight, permissions, and accountability.

As organizations continue adopting AI-assisted development and autonomous agent workflows, developers and technical teams need to understand how agents can support software planning, coding, testing, debugging, reviewing, automation, and delivery while staying safe, reliable, and aligned with business and technical goals. This course prepares you to understand agentic AI development concepts clearly and apply them in practical software development environments.

By working through practical scenarios and exam-aligned topics, you will build the knowledge required to succeed in the GitHub Agentic AI Developer certification exam and confidently understand how agentic AI systems are used in real-world development workflows.

What Students Will LearnPrepare Agent Architecture and SDLC Processes (15–20%)Understand how AI agents are designed and integrated into the software development lifecycle. Learn how to define agent goals, responsibilities, inputs, outputs, success criteria, task boundaries, workflow steps, and the role agents can play in planning, coding, testing, reviewing, and delivery processes.

Implement Tool Use and Environment Interaction (20–25%)Learn how agents interact with development tools, repositories, files, terminals, APIs, external systems, and software environments. Explore tool permissions, execution contexts, scoped access, safe tool usage, environment constraints, and how agents perform actions while staying within controlled boundarie

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