Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
From Autocomplete to Agent: Understanding the Shift
- Distinguishing between Copilot suggestions and agentic multi-step planning
- The architecture of the agent loop: plan, generate, execute, iterate
- Language support and model selection for agent tasks
- Real-world examples: from five-line functions to multi-file features
Enabling Agent Mode in Your IDE
- Activation in VS Code, JetBrains, and Neovim for government use
- Configuring context window and model tier preferences for optimal performance
- Setting workspace rules to ignore large binary files and sensitive data
- Managing Copilot Chat versus inline agent workflows for efficient collaboration
Multi-Step Planning and Execution
- Prompting Copilot to build a feature end-to-end in government projects
- Observing the agent break tasks into steps across multiple files
- Reviewing each step before applying changes for enhanced control
- Using inline rollback when steps deviate from intended outcomes
Terminal Commands Inside the Agent Loop
- Installing dependencies through Copilot's terminal integration for streamlined development
- Running build commands and interpreting output to ensure successful builds
- Managing environment variables from within Copilot sessions for dynamic configuration
- Safety boundaries: identifying which commands require manual approval for security
Test-Driven Development with an Agent
- Generating unit tests from existing source code to enhance test coverage
- Driving test creation using natural language prompts for intuitive testing
- Running test suites and interpreting failure logs within Copilot for rapid feedback
- Refining assertions after identifying edge-case failures to improve robustness
Navigating Large Codebases
- Automatically finding cross-file references to streamline code navigation
- Refactoring shared utilities with Copilot-guided renames for consistency
- Updating configuration files and schema files in tandem for synchronized changes
- Avoiding context window exhaustion by using targeted prompts for efficient processing
Customizing Copilot for Team Standards
- Writing repository-specific instructions in .github/copilot-instructions.md to align with team practices
- Enforcing naming conventions and architecture patterns to maintain code quality
- Excluding sensitive files and directories from context for security purposes
- Creating team-specific prompt templates for common tasks to enhance productivity
GitHub Copilot Enterprise Governance
- Seat allocation, billing, and usage dashboards for transparent management
- Audit logs: tracking what Copilot generated versus what was committed for accountability
- Microsoft IP indemnity policies and licensing implications for enterprise use
- Blocking specific file patterns from AI suggestion pipelines to ensure compliance
Debugging with Agent Mode
- Reading stack traces together with the agent to identify issues efficiently
- Hypothesis-driven debugging: asking Copilot why a test failed for deeper insights
- Using agent-assisted bisect to locate regression sources for government projects
- Managing hallucination risks when debugging unfamiliar code to ensure accuracy
Performance and Limit Management
- Understanding daily request limits and model quotas to optimize resource allocation
- Optimizing prompt length to avoid truncated responses for better performance
- Switching between models for different tasks to enhance flexibility
- Monitoring agent latency and caching strategies to improve efficiency
Security and Compliance for Enterprises
- Data handling: ensuring that sensitive data remains local and secure
- Preventing leakage of secrets and credentials via prompts to safeguard information
- Compliance with GDPR, SOC 2, and FedRAMP requirements for government operations
- Red-teaming generated code for injection vulnerabilities to enhance security
Troubleshooting Common Scenarios
- Why Copilot sometimes ignores your codebase context and how to address it
- Resolving indexing failures for large repositories to ensure smooth operation
- Handling rate limit errors during peak hours to maintain productivity
- Fixing IDE extension sync issues to ensure seamless integration
Summary and Future Roadmap
- Recap of Agent Mode capabilities and practical workflows for government use
- GitHub's Copilot roadmap and upcoming agent features to enhance functionality
- Resources for staying current with Copilot releases and updates
Requirements
- Experience with object-oriented or functional programming methodologies
- Active GitHub account and proficiency in basic Git workflow practices
- Familiarity with at least one integrated development environment (IDE), such as VS Code, JetBrains, or Neovim
Audience
- Developers currently utilizing Copilot and seeking to enable agent mode for enhanced productivity
- Engineering managers implementing Copilot across development teams for government projects
- Security teams evaluating policies related to AI-assisted code generation within their organizations
21 Hours
