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Course Outline
From Autocomplete to Agent: Understanding the Shift
- Differences between Copilot suggestions and agentic multi-step planning
- The agent loop architecture: plan, generate, execute, and iterate
- Language support and model selection for agent tasks
- Real-world examples: progressing from five-line functions to multi-file features
Enabling Agent Mode in Your IDE
- Activation procedures in VS Code, JetBrains, and Neovim
- Configuring context window size and model tier preferences
- Setting workspace rules and ignoring large binary files
- Managing workflows between Copilot Chat and inline agents
Multi-Step Planning and Execution
- Prompting Copilot to build a feature end-to-end
- Observing how the agent breaks tasks into steps across multiple files
- Reviewing each step before applying changes
- Using inline rollback capabilities when steps deviate
Terminal Commands Inside the Agent Loop
- Installing dependencies via Copilot’s terminal integration
- Running build commands and interpreting their output
- Managing environment variables directly 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 inside Copilot
- Refining assertions after observing edge-case failures
Navigating Large Codebases
- Automatically finding cross-file references
- Refactoring shared utilities with Copilot-guided renames
- Updating configuration and schema files simultaneously
- 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 architecture patterns
- Excluding sensitive files and directories from the context
- Creating team-specific prompt templates for common tasks
GitHub Copilot Enterprise Governance
- Seat allocation, billing, and usage dashboards
- Audit logs: tracking what Copilot generated versus what was committed
- 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 locate 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 caching strategies
Security and Compliance for Enterprises
- Data handling: what leaves your repository and what stays local
- Preventing leakage of secrets and credentials via prompts
- Compliance with GDPR, SOC 2, and FedRAMP requirements
- Red-teaming generated code for injection vulnerabilities
Troubleshooting Common Scenarios
- Reasons why Copilot may ignore your codebase context
- Resolving indexing failures for large repositories
- Handling rate limit errors during peak hours
- Fixing IDE extension sync 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 Integrated Development Environment (IDE) such as VS Code, JetBrains, or Neovim
Audience
- Developers currently using Copilot who aim to unlock agent mode capabilities
- Engineering managers overseeing the deployment of Copilot across development teams
- Security teams reviewing policies for AI-assisted code generation
21 Hours
