Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Treating AI coding tools merely as single-line autocomplete leaves most engineering productivity gains on the table, while ungoverned usage risks architectural drift and subtle security flaws. This programme teaches engineering teams how to integrate GitHub Copilot and autonomous coding agents into a disciplined software delivery workflow.

Multi-file refactoring with Copilot Edits and Agent Mode in the IDE, delegating issues to the asynchronous Copilot Coding Agent to open pull requests, encoding architectural standards in .github/copilot-instructions.md, connecting internal docs and schemas via MCP servers, and AI-assisted code review and security remediation are covered hands-on. Teams leave with a tailored repository instruction pack and coding-agent checklist.

Who Should Attend?

  • Frontend, backend, mobile and data/platform software engineers
  • Software architects, tech leads and Developer Experience (DevEx) teams
  • QA automation and DevSecOps engineers
  • Prerequisite: active software development and Git/GitHub workflow experience

Key Takeaways

  • Move beyond inline completions to multi-file refactoring and iterative problem-solving with IDE Agent Mode
  • Enforce team conventions and architecture rules using repository custom instructions and reusable prompt files
  • Extend the coding agent's context with MCP servers connected to internal schemas, docs and issue trackers
  • Scope and delegate GitHub issues to the autonomous Copilot Coding Agent and review its resulting pull requests
  • Accelerate unit/integration test generation, legacy code refactoring and vulnerability remediation

Curriculum

From Inline Autocomplete to Autonomous Coding Agents
  • Comparing inline completions, Chat, Multi-file Edits, IDE Agent Mode and background Coding Agents
  • Matching task complexity to the right interaction mode and underlying model (Claude, GPT, Gemini)
Context Engineering and Encoding Architectural Standards
  • Authoring .github/copilot-instructions.md, path-scoped rules and shared .prompt.md templates
  • Connecting database schemas, OpenAPI specs and internal docs to the IDE via MCP servers
Multi-File Development, Testing and Refactoring in Agent Mode
  • The plan-edit-test loop: letting the agent run terminal checks, linters and test suites
  • Refactoring legacy modules, generating edge-case unit tests and guided debugging
GitHub Copilot Coding Agent and Pull Request Workflows
  • Writing well-scoped GitHub Issues and acceptance criteria for asynchronous agent execution
  • Configuring the agent sandbox environment (copilot-setup-steps) and AI-assisted PR reviews
Security, Governance and Measuring Engineering Impact
  • Remediating CodeQL/SAST vulnerabilities with Copilot Autofix and preventing secret exposure
  • Content exclusions, IP indemnity settings and tracking impact with DORA and PR quality metrics

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