8781  Reviews star_rate star_rate star_rate star_rate star_half
$675USD
Duration 1 day
Course Code GH-600T00
Available Formats Classroom

Overview

Course Description

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.

Audience Profile

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
Read Less

Course Details

Course Details

Outline

  • Foundations of Agentic AI in GitHub
    • Define agentic AI in the SDLC
    • Explain the agent lifecycle - plan, act, evaluate
    • Describe GitHub as the system of record and control plane
    • Identify responsibilities, risks, anti-patterns, and traceability needs
    • Apply the contributor model to agent-generated work
  • Designing Agent Architecture and SDLC Integration
    • Map agent responsibilities to the SDLC
    • Define inputs, outputs, and success criteria
    • Separate planning, reasoning, and execution
    • Examples of implementing PR governance with templates, checks, CODEOWNERS, rules, and environment gates
    • Build reliable workflows - outputs, contexts, triggers, and cross-job handoffs
    • Control and operate agents - observability, tools, MCP, secrets, hooks, and reliability
  • Tooling, MCP, and Agent Execution Environments
    • How agents interact with GitHub APIs and workflows
    • Model Context Protocol (MCP) servers, registries, and allow lists
    • Execution context and boundaries
    • Agent execution limits and protections
    • Module assessment
  • Multi-Agent Systems and Orchestration
    • Define multi-agent responsibilities in the SDLC
    • Orchestrate agents using GitHub workflows
    • Isolate execution - branches, workflows, permissions, and concurrency
    • Detect and resolve conflicts using GitHub-native arbitration
    • Make the system observable - attribution, evidence, and handoffs
    • Operate reliably at scale - diagnose failures and recover safely
  • Memory, State, and Evaluation
    • Implement agent memory strategies
    • Persist agent state and manage context drift
    • Ensure continuity of agent memory and state across tools and environments
    • Define evaluation signals and enforce quality gates
    • Analyze agent failures and improve behavior
  • Governance, guardrails, and operations
    • Define risk-based autonomy and action boundaries
    • Enforce governance with GitHub controls
    • Design human-in-the-loop workflows
    • Control agent capabilities using least privilege
    • Make actions observable, traceable, and auditable
    • Maintain governance and operational reliability
    • Knowledge check

Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information
Ascendient Learning would like to occasionally share news about our services and career opportunities via e-mail when we believe it would be relevant to you. Would you like to opt-in to these communications? You may withdraw your consent at any time using the opt-out/preferences link in the emails sent.

FAQ

How do I get a Microsoft exam voucher?

Pearson Vue Exam vouchers can be requested and ordered with your course purchase.

  • Vouchers are non-refundable and non-returnable. Vouchers expire 12 months from the date they are issued unless otherwise specified in the terms and conditions.
  • Voucher expiration dates cannot be extended. The exam must be taken by the expiration date printed on the voucher.

Do Microsoft courses come with post lab access?

Most Microsoft official courses will include post-lab access ranging from 30 to 180 calendar days after instructor led course delivery. A lab training key in class will be provided that can be leveraged to continue connecting to a remote lab environment for the individual course attendee.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour-long after 3-3.5 hours of the class day.

What languages are used to deliver training?

Microsoft courses are conducted in English unless otherwise specified.

Reviews

This course is important because we work at CSOC and it's applicable to our daily work.

ExitCertified provided a very organized way to learn and provided materials to follow along.

my experince was great from the day i regetered to the actuall day of the class.

The course was informative, and I learnt a new skill. The instructor was up to the point.

Good training. A lot to take in for the short amount of time we have though