Foundations of AI Coding Agents with GitHub Copilot

Run GitHub Copilot as a capable AI coding agent rather than an autocomplete tool. AI coding agent architecture covers context windows, token limits, the plan-execute-iterate cycle, autonomous file...

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$1,665USD
Duration 2 days
Course Code AIC-1102
Available Formats Classroom, Virtual
Next Class: Sep 17, 2026

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Overview

Course Description

Run GitHub Copilot as a capable AI coding agent rather than an autocomplete tool. AI coding agent architecture covers context windows, token limits, the plan-execute-iterate cycle, autonomous file and terminal operations, and the permission boundaries that keep agentic operation safe. Context engineering covers prompting strategies, file and codebase reference systems, extended-thinking budgets, and cross-session memory that shapes agent behaviour over a project’s lifetime. Configuration standards, the Model Context Protocol, and practical workflows cover copilot-instructions.md, AGENTS.md, exclusion strategies, MCP hosts and servers, permission management, code generation, review, testing, debugging, and refactoring. GitHub Copilot product features cover Chat, Edits, Agent Mode, the Coding Agent, Spaces, Extensions, and platform integration. Hands-on labs produce a configured environment, a project-level copilot-instructions.md, at least one MCP integration, and a working agent-driven workflow. The course is designed for software developers, data scientists, and anyone integrating an AI coding agent into daily team workflows.

Skills Gained

By the end of this course, participants will be able to:

  • Apply prompt engineering and context-management techniques to GitHub Copilot
  • Configure GitHub Copilot for IDE workflows and per-project conventions
  • Drive code generation, refactoring, testing, and debugging through Copilot’s agentic operation
  • Integrate Model Context Protocol servers to extend Copilot’s reach
  • Evaluate Copilot’s output against project quality and security standards

Who Can Benefit

This course is designed for:

  • Software Developers
  • Data Scientists & Analysts
  • Anyone interested in coding agents

Prerequisites

Participants should enter this course with:

  • Familiarity using Generative AI
  • Practical Python experience

Organizational Objectives

This course assists organizations to:

  • Accelerate first-draft code production through agent-driven generation, refactoring, and test scaffolding
  • Reduce review cycles by codifying tool conventions in copilot-instructions.md and shared configuration
  • Keep AI tool use inside permission boundaries via MCP server allowlists and reviewed config hierarchies
  • Build a team-wide working knowledge of GitHub Copilot in two days

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - AI Coding Agent Foundations

By the end of this module, you will be able to describe how modern AI coding agents are architected, recognize the plan-execute-iterate loop in action, and apply the permission and review controls that keep autonomous file, terminal, and Git operations safe to delegate.

  • AI coding agent architecture and components
  • Context windows and token limits
  • Agentic operation and the plan-execute-iterate cycle
  • Autonomous file, terminal, and Git operations
  • Reviewing and accepting proposed changes
  • Tool permissions and safety controls
  • Hands-on Lab: Walk a coding agent through a small refactor end-to-end, exercising the permission and review controls before any change is committed.

Module 2 - Context Engineering

By the end of this module, you will be able to apply zero-shot, few-shot, and chain-of-thought prompting to a coding agent, attach relevant code and docs through reference systems, and budget extended thinking and persistent memory so the agent stays focused on the task.

  • Context management fundamentals
  • Prompting techniques (zero-shot, few-shot, chain-of-thought)
  • Reference systems (@file, @codebase, @docs, @web)
  • Extended thinking modes and budget allocation
  • Memory and cross-session persistence
  • Hands-on Lab: Refine a vague coding request into a focused prompt that pulls in the right reference context and budgets extended thinking appropriately.

Module 3 - Configuration Standards

By the end of this module, you will be able to author a project-level configuration file (CLAUDE.md, AGENTS.md, or tool-specific equivalent), organize the configuration hierarchy under version control, and apply exclusion strategies to keep sensitive material out of agent context.

  • Configuration file hierarchy and version control
  • Universal context configuration (e.g., CLAUDE.md) and cross-tool compatibility
  • AGENTS.md format and conventions
  • Tool-specific configuration files
  • File exclusion strategies
  • Hands-on Lab: Author a project-level CLAUDE.md (or AGENTS.md) that captures the team’s conventions, exclusions, and review expectations.

Module 4 - Model Context Protocol (MCP)

By the end of this module, you will be able to describe the MCP host, client, and server roles, install and configure an MCP server, recognize the primitives the protocol exposes, and apply permission management appropriate to the tools the server grants access to.

  • MCP architecture (hosts, clients, servers)
  • MCP primitives (tools, resources, prompts)
  • Configuration and server installation
  • Security considerations and permission management
  • Hands-on Lab: Install one MCP server, exercise its primitives from a coding agent, and audit the permission surface it introduces.

Module 5 - Practical Workflows

By the end of this module, you will be able to drive a coding agent across realistic tasks — generation, review, testing, debugging, refactoring, documentation, and Git operations — and recognize where agentic operation belongs inside an existing development workflow.

  • Code generation and multi-file scaffolding
  • Automated code review and security scanning
  • Test generation and test-driven workflows
  • Agent-driven debugging and error diagnosis
  • Refactoring patterns (single-file and multi-file)
  • Documentation generation and Git integration with automated commit messages
  • Hands-on Lab: Run a single feature ticket through generation, review, test, and documentation steps, then have the agent prepare the commit.

Module 6 - GitHub Copilot Features

By the end of this module, you will be able to choose between Copilot Chat, Edits, and Agent Mode for a given task, attach context through Spaces and Extensions, configure copilot-instructions.md for project conventions, and integrate Copilot with GitHub PRs and Issues.

  • Model selection (GPT, Claude, Gemini) and optimization
  • Copilot Chat, inline chat, and Copilot Edits
  • Agent Mode and Copilot Coding Agent (Issues to PRs)
  • Copilot Spaces, Extensions (@azure, @docker, …), and context collections
  • Workspace indexing (remote, local, basic) and copilot-instructions.md
  • GitHub platform integration (PRs, Issues, Code Review)
  • Hands-on Lab: Configure Copilot for a small project with copilot-instructions.md and drive a feature from Issue through Coding Agent to merged PR.

Schedule

2 options available

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

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GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

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Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

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As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

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It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

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We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

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Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

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Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

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Reviews

Great company -- easy to sign up and very organized. Loved my teacher and class overall.

Very good couse and again we would like to see more videos on removing FRUs

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

I found this course informative. It was easy to follow and provided some good information.

very good and spcecific course and above all a very good instructor. In few days I have learned a lot.