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GH-600: GitHub Certified Agentic AI Developer Video Course
New
2 students

GH-600: GitHub Certified Agentic AI Developer Video Course

The first full video course for GH-600: agents in the SDLC, MCP, agents in CI, memory, evals, orchestration, guardrails
Last updated 8/2026
English

What you'll learn

  • Map every GH-600 domain to the concrete decision an agent forces during real work
  • Choose agent work by shape: bounded input, checkable output, a done a machine can check
  • Configure tools, MCP servers, allow lists, and CI lanes with least privilege
  • Diagnose agent failures from logs, plans, and traces, then fix the layer that failed
  • Coordinate several agents with orchestration patterns, isolation lanes, and collision detection
  • Attach autonomy to action classes and guard irreversible actions with explicit authorization

Course content

8 sections12 lectures1h 49m total length
  • Exam decoded: one agent's workday is the whole syllabus9:52
    The six domains mapped to six moments in one agent's workday, and the two elimination rules pulled from the study guide's own wording.

Requirements

  • Working knowledge of Git and GitHub: branches, pull requests, reviews
  • No agent experience required; every concept is taught from its failure case first

Description

The video course for GH-600. Most GH-600 prep is a stack of practice questions with no teaching behind it. This course teaches the exam: all six domains of the GitHub Certified Agentic AI Developer certification, each taught through a real failure an agent caused, then worked as an exam question with the wrong options killed one by one.

What makes it different:

  • Six domains as one story: one agent, one repository, one workday. What runs before the agent acts, the tools in its hands, what it remembers, the reckoning when its work is judged, the colleagues it coordinates with, and the rules of the house.

  • Two elimination rules pulled from the study guide's own wording: a prompt is not a permission, and size the gate to the risk. Name the risk first and four plausible options collapse to two.

  • A worked question inside every lesson, in the exam's own scenario style.

  • Concept-first, so it stays current: no UI walkthroughs that go stale, no version trivia.

Full coverage at the weights the study guide publishes:

  • Domain 1, agent architecture and SDLC integration (15 to 20 percent): lessons 2 and 3

  • Domain 2, tool use and environment interaction (20 to 25 percent, the largest domain): lessons 4, 5, and 6

  • Domain 3, memory, state, and execution management (10 to 15 percent): lesson 7

  • Domain 4, evaluation, error analysis, and tuning (15 to 20 percent): lessons 8 and 9

  • Domain 5, multi-agent coordination (15 to 20 percent): lessons 10 and 11

  • Domain 6, guardrails and accountability (10 to 15 percent): lesson 12

The exam: 120 minutes, 700 of 1000 to pass, renewed yearly through a free online assessment.

Two full timed mocks are included: 56 fresh questions each at the published blueprint weights, 120 minutes, every option explained and cited to the study guide. Sit them after the lessons and walk into the real exam knowing your score.

Start with the free preview lesson: the whole exam decoded as one agent's workday.

AI disclosure: AI tools were used in producing this course, including the practice questions. The curriculum, the exam facts, and every lesson were verified by the instructor against the current official exam guide, and every practice question cites the study guide passage it tests.

Who this course is for:

  • Developers preparing for the GH-600 GitHub Certified Agentic AI Developer exam
  • Engineers bringing coding agents into team workflows who need the governance patterns the exam tests