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Course Outline
From Autocomplete to Agent: Understanding the Shift
- Differences between standard Copilot suggestions and agentic multi-step planning.
- Architecture of the agent loop: plan, generate, execute, 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.
- Setting workspace rules and excluding large binary files.
- Distinguishing between Copilot Chat and inline agent workflows.
Multi-Step Planning and Execution
- Prompting Copilot to develop a feature end-to-end.
- Monitoring how the agent breaks tasks into steps across multiple files.
- Reviewing each step before applying changes.
- Using inline rollback mechanisms when steps deviate from the plan.
Terminal Commands Inside the Agent Loop
- Installing dependencies via Copilot’s terminal integration.
- Running build commands and interpreting output logs.
- Managing environment variables within Copilot sessions.
- 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 interpreting failure logs directly in Copilot.
- Refining assertions after identifying edge-case failures.
Navigating Large Codebases
- Automatically finding cross-file references.
- Refactoring shared utilities with Copilot-guided renaming.
- Synchronously updating configuration and schema files.
- Avoiding 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 analysis.
- Creating team-specific prompt templates for common tasks.
GitHub Copilot Enterprise Governance
- Seat allocation, billing management, and usage dashboards.
- Audit logs: tracking Copilot’s generation 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 alongside the agent.
- Hypothesis-driven debugging: asking Copilot why a test failed.
- Using agent-assisted bisect to identify regression sources.
- Managing hallucination risks when debugging unfamiliar code.
Performance and Limit Management
- Understanding daily request limits and model quotas.
- Optimizing prompt length to prevent truncated responses.
- Switching between models for different task types.
- Monitoring agent latency and implementing caching strategies.
Security and Compliance for Enterprises
- Data handling protocols: what leaves your repository versus what remains local.
- Preventing leakage of secrets and credentials through prompts.
- Compliance with GDPR, SOC 2, and FedRAMP requirements.
- Red-teaming generated code to identify injection vulnerabilities.
Troubleshooting Common Scenarios
- Understanding why Copilot may occasionally ignore codebase context.
- Resolving indexing failures in large repositories.
- Handling rate limit errors during peak usage hours.
- Fixing IDE extension synchronization issues.
Summary and Future Roadmap
- Recap of Agent Mode capabilities and practical workflows.
- GitHub’s Copilot roadmap and upcoming agent features.
- Resources for staying current with Copilot releases.
Requirements
- Experience with object-oriented or functional programming.
- A GitHub account and fundamental knowledge of Git workflows.
- Familiarity with at least one IDE (VS Code, JetBrains, or Neovim).
Audience
- Developers currently using Copilot who wish to enable and utilize Agent Mode.
- Engineering managers deploying Copilot across development teams.
- Security teams evaluating policies for AI-assisted code generation.
21 Hours
