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Course Outline
From Autocomplete to Agent: Grasping the Paradigm Shift
- Distinguishing Copilot suggestions from agentic multi-step planning
- Understanding the architecture of the agent loop: plan, generate, execute, and iterate
- Language support options and model selection for agent tasks
- Practical examples: transitioning from five-line functions to multi-file features
Enabling Agent Mode in Your IDE
- Activation procedures for VS Code, JetBrains, and Neovim
- Configuring context windows and model tier preferences
- Establishing workspace rules and ignoring large binary files
- Managing workflows between Copilot Chat and inline agents
Multi-Step Planning and Execution
- Prompting Copilot to build features from start to finish
- Observing the agent break down tasks into steps across multiple files
- Reviewing each step prior to applying changes
- Utilizing inline rollback when steps deviate from the plan
Terminal Commands Within the Agent Loop
- Installing dependencies via Copilot's terminal integration
- Executing build commands and interpreting their output
- 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 interpreting failure logs inside Copilot
- Refining assertions after analyzing edge-case failures
Navigating Large Codebases
- Automatically locating cross-file references
- Refactoring shared utilities with Copilot-guided renaming
- Updating configuration and schema files simultaneously
- Preventing context window exhaustion through targeted prompting
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
- Creating team-specific prompt templates for routine tasks
GitHub Copilot Enterprise Governance
- Seat allocation, billing management, and usage dashboards
- Audit logs: tracking what Copilot generated versus what was committed
- Microsoft IP indemnity policies and their licensing implications
- Blocking specific file patterns from AI suggestion pipelines
Debugging with Agent Mode
- Analyzing stack traces collaboratively with 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 avoid truncated responses
- Switching between models for different tasks
- Monitoring agent latency and implementing caching strategies
Security and Compliance for Enterprises
- Data handling: identifying what leaves your repository and what remains local
- Preventing leakage of secrets and credentials via prompts
- Ensuring compliance with GDPR, SOC 2, and FedRAMP requirements
- Red-teaming generated code for injection vulnerabilities
Troubleshooting Common Scenarios
- Understanding why Copilot might ignore your codebase context
- Resolving indexing failures for 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 in object-oriented or functional programming
- A GitHub account along with fundamental knowledge of Git workflows
- Familiarity with at least one Integrated Development Environment (IDE) such as VS Code, JetBrains, or Neovim
Target Audience
- Developers currently using Copilot who aim to unlock its agent mode capabilities
- Engineering managers overseeing the adoption of Copilot across development teams
- Security teams evaluating policies for AI-assisted code generation
21 Hours
