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Course Outline
From Autocomplete to Agent: Understanding the Paradigm Shift
- Differentiating Copilot suggestions from agentic multi-step planning.
- The architecture of the agent loop: plan, generate, execute, and iterate.
- Language support and model selection for agent-driven tasks.
- Real-world examples: progressing from five-line functions to multi-file features.
Enabling Agent Mode in Your IDE
- Activation procedures for VS Code, JetBrains, and Neovim.
- Configuring context window sizes and model tier preferences.
- Defining workspace rules and excluding large binary files.
- Distinguishing between Copilot Chat and inline agent workflows.
Multi-Step Planning and Execution
- Prompting Copilot to build a feature from start to finish.
- Observing how the agent decomposes tasks across multiple files.
- Reviewing each step prior to applying changes.
- Utilizing inline rollback mechanisms when steps deviate from the intended path.
Terminal Commands Inside the Agent Loop
- Installing dependencies via Copilot’s terminal integration.
- Executing build commands and interpreting output logs.
- Managing environment variables directly within Copilot sessions.
- Understanding safety boundaries: identifying commands that require manual approval.
Test-Driven Development with an Agent
- Generating unit tests from existing source code.
- Driving test creation using natural language prompts.
- Running test suites and analyzing failure logs within Copilot.
- Refining assertions after observing edge-case failures.
Navigating Large Codebases
- Automatically discovering cross-file references.
- Refactoring shared utilities with Copilot-guided renaming.
- Synchronously updating configuration and schema files.
- Preventing context window exhaustion through targeted prompts.
Customizing Copilot for Team Standards
- Writing repository-specific instructions in .github/copilot-instructions.md.
- Enforcing naming conventions and architectural patterns.
- Excluding sensitive files and directories from context processing.
- Developing team-specific prompt templates for routine tasks.
GitHub Copilot Enterprise Governance
- Seat allocation, billing management, and usage dashboards.
- Audit logs: tracking generated content versus committed code.
- Microsoft IP indemnity policies and licensing implications.
- Blocking specific file patterns from AI suggestion pipelines.
Debugging with Agent Mode
- Analyzing stack traces collaboratively with the agent.
- Hypothesis-driven debugging: querying Copilot on test failures.
- Utilizing agent-assisted bisect methods to identify regression sources.
- Mitigating hallucination risks when debugging unfamiliar code.
Performance and Limit Management
- Understanding daily request limits and model quotas.
- Optimizing prompt length to prevent truncated responses.
- Selecting appropriate models for different task types.
- Monitoring agent latency and implementing caching strategies.
Security and Compliance for Enterprises
- Data handling: determining what data leaves the repository versus what remains local.
- Preventing the leakage of secrets and credentials through prompts.
- Ensuring compliance with GDPR, SOC 2, and FedRAMP requirements.
- Conducting red-team exercises on generated code for injection vulnerabilities.
Troubleshooting Common Scenarios
- Diagnostics for why Copilot may ignore codebase context.
- Resolving indexing failures in large repositories.
- Managing rate limit errors during peak usage hours.
- Correcting IDE extension synchronization issues.
Summary and Future Roadmap
- Recap of Agent Mode capabilities and practical workflows.
- Overview of GitHub's Copilot roadmap and upcoming agent features.
- Resources for staying updated with the latest Copilot releases.
Requirements
- Experience with object-oriented or functional programming paradigms.
- A GitHub account and foundational knowledge of Git workflows.
- Familiarity with at least one integrated development environment (VS Code, JetBrains, or Neovim).
Target Audience
- Developers currently utilizing Copilot who wish to unlock agent mode capabilities.
- Engineering managers overseeing the deployment of Copilot across development teams.
- Security teams evaluating policies for AI-assisted code generation.
21 Hours
