- Choosing the steps an agent should perform and avoiding common agent anti-patterns.
- Defining inputs, outputs and success criteria for agents.
- Keeping planning separate from execution, with structured plans that are validated before the agent acts.
- Setting the degree of autonomy, inspectable artifacts and human intervention that does not slow delivery.
GitHub Certification Training Course
GitHub Agentic AI Developer (GH-600) Certification Training Course
Run AI agents safely inside your software delivery process, with GitHub as the control plane, and get ready for the GitHub Agentic AI Developer (GH-600) exam with Accumentum’s live online training.
Course focus: Plan, configure, evaluate and govern AI agents inside GitHub workflows, from tools and MCP servers to memory, multi-agent orchestration and guardrails, and prepare for the GitHub Agentic AI Developer (GH-600) exam.
Course Overview
Operate, supervise and govern AI agents inside a real software delivery process.
AI agents can now open branches, write code and raise pull requests on their own. This course is for the engineers who run those agents in a real SDLC and have to keep them reliable and safe. We use GitHub as the system of record and control plane for everything the agents do.
The course follows the official GH-600 skills outline. You will plan agent work and keep planning separate from action, configure tools and MCP servers, scope agents to repositories and CI workflows, manage memory and state, evaluate and tune agent output, coordinate multiple agents, and set guardrails with human review where it counts.
By the end you will know how to give agents enough autonomy to be useful without losing control or traceability. You will also be ready to sit the GitHub Agentic AI Developer exam.
Course Objectives
Build agentic AI skills and GH-600 exam readiness.
01
Agent Architecture
Define what agents should do in the SDLC, with clear inputs, outputs, success criteria and structured plans.
02
Tools and MCP
Configure agent tools, permissions and MCP servers, and scope agents to repositories, branches and CI workflows.
03
Evaluation and Orchestration
Evaluate agent output, trace failures to root causes, and coordinate multiple agents without conflicts.
04
GH-600 Exam Readiness
Cover all six domains in the official GH-600 skills outline, including memory, state and guardrails.
Who Should Attend
- Software engineers who run coding agents such as GitHub Copilot inside their team’s SDLC.
- Platform and DevOps engineers who wire agents into CI workflows and need them to fail safely.
- Architects and security engineers who decide how much autonomy agents get and where humans step in.
- Application developers and product managers who work alongside agents and want to understand how they are governed.
Prerequisites
- There are no formal prerequisites for the GH-600 exam or for this course.
- GitHub recommends reviewing the related Microsoft Learn modules before the exam. We cover the same skills in class.
- You should have experience with the SDLC, GitHub workflows and controls, and code quality, security and review practices.
- Hands-on time with coding agents such as GitHub Copilot, MCP servers and agent customization like custom instructions, custom agents, tools and Copilot setup steps will help.
Pathway Map
The GitHub Agentic AI Developer course follows the six GH-600 skill domains.
Each module maps to an official exam domain and its published weight, from agent architecture to guardrails and accountability.
Course Content
- Identifying and configuring agent tools and tool permissions.
- Adding MCP servers, configuring the GitHub remote MCP server, MCP registries and MCP allow lists.
- Scoping agents to a repository or branch, invoking them in CI workflows and letting them create branches and pull requests.
- Error handling, retries, rollbacks, escalation paths and traceability for agent actions.
- Choosing between short-term, long-term and external memory and scoping it to the task.
- Rules for memory expiration, pruning and reset.
- Capturing progress and decisions as durable artifacts so agents can resume without repeating work.
- Detecting drift and preventing stale or conflicting context across tools and environments.
- Setting expected outcomes, constraints and evaluation signals for agent tasks.
- Generating evaluation signals with automated scanning tools.
- Finding failures in logs, plans, traces and workflow artifacts and classifying root causes.
- Tuning instructions, workflows, memory and tool access based on results.
- Applying an orchestration pattern and isolating agents for parallel work.
- Resolving overlapping changes, duplicated effort and contradictory output.
- Producing artifacts and decision records that support review, audit and post-hoc analysis.
- Recovering from failed or stalled runs and adding, updating or retiring agents without breaking workflows.
- Classifying agent actions by operational, security and compliance risk.
- Assigning autonomy levels that keep delivery fast and stay within Responsible AI standards.
- Blocking actions that break policy and scoping permissions to least privilege.
- Requiring explicit approval for irreversible changes while skipping approvals that do not reduce risk.
Course Features
Built for practical agentic AI training and GH-600 exam prep.
Interactive Learning
Participate in training sessions, discussions, and hands-on labs with experienced instructors.
Comprehensive Study Materials
Access extensive resources, including e-books, video lectures, and practice exams.
Practical Scenarios
Engage in real-world exercises and case studies to apply GitHub Agentic AI Developer concepts.
Certification Preparation
Receive guidance and tips to successfully pass the GitHub Agentic AI Developer exam.
Certification Exam
Prepare for the official GitHub Agentic AI Developer exam.
GH-600 Exam Readiness
Accumentum’s GitHub Agentic AI Developer course follows the official GH-600 skills outline and prepares you for every domain on the exam.
After the course you will be ready to take the official GitHub Agentic AI Developer (GH-600) exam. GitHub says it validates your ability to operate, supervise and govern autonomous AI agents within GitHub-driven SDLC workflows.
The exam is intermediate level and runs 120 minutes. You can take it at a Pearson VUE testing center or online. It is currently offered in English. A score of 700 or higher is required to pass.
The certification is valid for 2 years. GitHub is moving to Microsoft’s recertification process, which will let you keep your certification current without retaking the full exam.
Skills Measured
What Microsoft says GH-600 measures.
Microsoft publishes the skills measured for Exam GH-600. Here are all 6 domains, 19 skill groups and 65 tasks in Microsoft's own wording. Our course maps a lesson or lab to each one.
Integrate agents into the software development lifecycle (SDLC)
Part of Prepare agent architecture and SDLC processes (15 to 20%)
- Identify steps for agents to perform
- Identify and mitigate common anti-patterns in agents
- Define inputs, outputs, and success criteria for agents
Define boundaries between planning, reasoning, and action
Part of Prepare agent architecture and SDLC processes (15 to 20%)
- Configure agent planning to be distinct from agent execution
- Configure an agent to output a structured plan
- Validate agent plans
- Prevent agent action until the agent checked and approved
Configure observability and control for autonomous agents
Part of Prepare agent architecture and SDLC processes (15 to 20%)
- Plan and implement the degree of agent autonomy, including guardrails
- Configure agent to produce inspectable artifacts within standard development tooling
- Configure human intervention for autonomous agents without slowing delivery
Select and configure agent tools
Part of Implement tool use and environment interaction (20 to 25%)
- Identify required tools
- Configure agent tools
- Configure agent tool permissions
Configure MCP servers
Part of Implement tool use and environment interaction (20 to 25%)
- Add an MCP server as a tool to an agent
- Configure a GitHub remote MCP server
- Configure the MCP registries
- Configure MCP allow lists
Integrate agents within development environments
Part of Implement tool use and environment interaction (20 to 25%)
- Evaluate the execution context for an agent
- Configure an agent's scope to a specific repository
- Configure an agent to be invoked in a CI workflow
- Configure an agent to use branch-based scope
- Enable an agent to perform autonomous actions, including creating branches and pull requests
- Configure an agent to handle environment-specific constraints
Operate agents with safe execution paths and robust error handling
Part of Implement tool use and environment interaction (20 to 25%)
- Implement error handling
- Implement retries
- Implement rollbacks
- Implement escalation paths
- Implement traceability and accountability for agent actions
Implement agent memory strategies
Part of Manage memory, state, and execution (10 to 15%)
- Choose between short-term, long-term, and external memory
- Scope agent memory to task-relevant information
- Define memory expiration, pruning, and reset rules
Persist agent state and manage context drift
Part of Manage memory, state, and execution (10 to 15%)
- Capture task progress and decisions as durable artifacts
- Resume agent work without repeating steps or diverging from prior decisions
- Detect and correct drift during extended agent execution
Ensure continuity of agent memory and state across tools and environments
Part of Manage memory, state, and execution (10 to 15%)
- Share agent state
- Prevent conflicting context
- Prevent stale context
Define success criteria and evaluation signals for agent tasks
Part of Perform evaluation, error analysis, and tuning (15 to 20%)
- Specify expected outcomes and operational constraints for agent tasks
- Identify qualitative and quantitative evaluation signals to evaluate agents
- Align evaluation criteria with development intent
- Generate evaluation signals by using automated scanning tools
Analyze agent failures and identify root causes
Part of Perform evaluation, error analysis, and tuning (15 to 20%)
- Identify failures by using logs, plans, traces, outputs, and workflow artifacts
- Classify root causes, including reasoning errors, tool misuse, and context or environment issues
Tune agent behavior based on evaluation results
Part of Perform evaluation, error analysis, and tuning (15 to 20%)
- Revise instructions, workflows, or constraints
- Refine memory usage
- Refine tool usage and tool access
Operate and manage multi-agent workflows
Part of Orchestrate multi-agent coordination (15 to 20%)
- Apply an orchestration pattern to coordinate multiple agents
- Configure agent isolation for parallel execution
- Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
Part of Orchestrate multi-agent coordination (15 to 20%)
- Configure multi-agent workflows to produce artifacts suitable for review and audit
- Document key decisions, handoffs, and outcomes across agents
- Perform post-hoc analysis of multi-agent behavior
Detect and respond to multi-agent failures and degraded behavior
Part of Orchestrate multi-agent coordination (15 to 20%)
- Identify failed, partial, or stalled agent executions
- Respond to degraded behavior or coordination across agents
- Implement multi-agent recovery patterns, including rollback and human-in-the-loop
Manage the lifecycle of agents within multi-agent workflows
Part of Orchestrate multi-agent coordination (15 to 20%)
- Add agents to existing multi-agent workflows
- Update, reconfigure, or replace agents without disrupting active workflows
- Retire agents while preserving auditability and workflow continuity
Define autonomy levels
Part of Implement guardrails and accountability (10 to 15%)
- Classify agent actions by operational, security, and compliance risk to right-size human interventions
- Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards
Implement guardrails and human-in-the-loop workflows
Part of Implement guardrails and accountability (10 to 15%)
- Identify the subset of actions that require human judgment
- Block actions that violate defined security, compliance, or Responsible AI policies
- Scope permissions and execution contexts to enforce least-privilege access
- Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
- Preserve execution velocity by minimizing approvals that do not materially reduce risk
Who It Is For
The GH-600 candidate profile.
Here's how Microsoft describes the person this exam is built for. 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.
Microsoft lists these responsibilities for the role: 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 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.
Study Plan
A practical path to GH-600.
We open with Prepare agent architecture and SDLC processes, which Microsoft weights at 15 to 20%. Its skill groups are "Integrate agents into the software development lifecycle (SDLC)", "Define boundaries between planning, reasoning, and action" and "Configure observability and control for autonomous agents".
Next comes Implement tool use and environment interaction, worth 20 to 25% of the exam. Microsoft breaks it into "Select and configure agent tools", "Configure MCP servers", "Integrate agents within development environments" and "Operate agents with safe execution paths and robust error handling".
After that we move to Manage memory, state, and execution, which counts for 10 to 15%. You'll see it split into "Implement agent memory strategies", "Persist agent state and manage context drift" and "Ensure continuity of agent memory and state across tools and environments".
Then we take on Perform evaluation, error analysis, and tuning, at 15 to 20% of your score. Inside it are "Define success criteria and evaluation signals for agent tasks", "Analyze agent failures and identify root causes" and "Tune agent behavior based on evaluation results".
We follow with Orchestrate multi-agent coordination, which Microsoft weights at 15 to 20%. The groups here are "Operate and manage multi-agent workflows", "Configure observability for multi-agent behavior by using logs, artifacts, and operational signals", "Detect and respond to multi-agent failures and degraded behavior" and "Manage the lifecycle of agents within multi-agent workflows".
We close with Implement guardrails and accountability, worth 10 to 15% of the exam. It holds "Define autonomy levels" and "Implement guardrails and human-in-the-loop workflows".
Implement tool use and environment interaction carries the most weight, so it gets the most lab time. In the last week you take Microsoft's free practice assessment, review whatever it flags and try the exam sandbox so the interface holds no surprises.
Exam Policies
What to know before you book GH-600.
GH-600 leads to the GitHub Certified: Agentic AI Developer certification. Microsoft requires a score of 700 or greater to pass, and you get 120 minutes to complete the exam. It's proctored and may include interactive components.
The exam is offered in English. If it isn't available in your preferred language, Microsoft lets you request an extra 30 minutes. You can also request accommodations if you use assistive devices or need extra time.
If you don't pass, you can retake the exam 24 hours after your first attempt. Waits for later retakes vary under Microsoft's retake policy. Exam price depends on the country or region where the exam is proctored.
Microsoft notes that the bullets under each skill show how the skill is assessed, and related topics may also appear. Most questions cover generally available features, though Preview features can show up if they're commonly used.
See the GitHub Certified: Agentic AI Developer certification page on Microsoft Learn.
Career Outlook
Where GH-600 can take you.
The U.S. Bureau of Labor Statistics reports that the median annual wage for software developers, quality assurance analysts and testers was $134,040 in May 2025. Treat it as a reference point and not a promise. Location, experience and the employer drive what any one person earns. Source: BLS Occupational Outlook Handbook.
Funding and PathPay
Ways to pay for GH-600 training.
Paying on your own? GH-600 prep is eligible for PathPay, our monthly payment plan. Terms of 3, 4 and 6 months carry no added fee. 9, 12 and 18 month terms add 5%, 10% and 15%. Course access opens when your first payment clears.
Our funding page lists every other way to pay, and you can always talk with us or email info@accumentum.net before you enroll.
Frequently Asked Questions
Answers to common questions about Accumentum’s GitHub Agentic AI Developer course, the GH-600 exam, eligibility, delivery and payment.
It shows you can deploy, operate, integrate and govern AI agents in production SDLC workflows, using GitHub as the control plane. The exam code is GH-600.
Engineers who run AI agents in their software delivery process. That includes platform and DevOps engineers, architects, application developers and security engineers who work with agents.
There are no formal prerequisites. You should know the SDLC and GitHub workflows, and have hands-on time with coding agents like GitHub Copilot and MCP servers. GitHub recommends reviewing the related Microsoft Learn modules first.
Six domains: agent architecture and SDLC processes (15 to 20%), tool use and environment interaction (20 to 25%), memory, state and execution (10 to 15%), evaluation, error analysis and tuning (15 to 20%), multi-agent coordination (15 to 20%), and guardrails and accountability (10 to 15%).
The exam runs 120 minutes. You can take it at a Pearson VUE testing center or online. It is currently offered in English.
You need a score of 700 or higher.
It is valid for 2 years. GitHub is moving to Microsoft’s recertification process, which will let you renew without retaking the full exam.
The course is delivered live online by an instructor, with hands-on practice in GitHub between sessions.
Yes. PathPay lets you pay for your training over time instead of all at once. You can enroll through PathPay from this page or contact us with any questions.
Microsoft lists these domains:
- Prepare agent architecture and SDLC processes, 15 to 20%
- Implement tool use and environment interaction, 20 to 25%
- Manage memory, state, and execution, 10 to 15%
- Perform evaluation, error analysis, and tuning, 15 to 20%
- Orchestrate multi-agent coordination, 15 to 20%
- Implement guardrails and accountability, 10 to 15%
Implement tool use and environment interaction is the largest at 20 to 25%. We give it the most lab time.
You can retake it 24 hours after your first attempt. Later retakes have longer waits under Microsoft's retake policy.
Yes. Microsoft offers a free practice assessment, plus an exam sandbox that shows you the exam interface.
Enrollment
Enroll in the GitHub Agentic AI Developer (GH-600) Certification Training Course with Accumentum.
Enroll in the GitHub Agentic AI Developer (GH-600) Certification Training Course with Accumentum to learn how to run AI agents in your SDLC without losing control. You will cover every GH-600 domain, from agent planning, tools and MCP servers to memory, evaluation, multi-agent orchestration and guardrails.
When you finish, you will be ready to sit the GitHub Agentic AI Developer exam and bring agents into your delivery process with confidence. For upcoming class dates and to save your seat, use the registration page linked below.
Put AI agents to work safely with GitHub Agentic AI Developer training.
Prepare for the GitHub Agentic AI Developer (GH-600) exam and learn to run agents in your SDLC with the right tools, checks and guardrails.