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Course Outline
From Autocomplete to Agent: Understanding the Shift
- Distinctions between Copilot autocomplete and autonomous multi-step planning
- Agent loop architecture: planning, generation, execution, and iteration
- Language compatibility and model selection for agent-based tasks
- Practical applications: transitioning from simple functions to complex, multi-file features
Enabling Agent Mode in Your IDE
- Activation procedures for VS Code, JetBrains, and Neovim
- Configuration of context windows and model tier preferences
- Establishment of workspace rules and exclusion of large binary files
- Differentiation between Copilot Chat and inline agent workflows
Multi-Step Planning and Execution
- Prompting strategies for end-to-end feature development
- Observation of task decomposition across multiple files
- Validation of each step prior to applying changes
- Utilization of inline rollback mechanisms for course correction
Terminal Commands Inside the Agent Loop
- Dependency installation via Copilot terminal integration
- Execution of build commands and interpretation of system output
- Management of environment variables within Copilot sessions
- Establishment of safety boundaries and manual approval requirements
Test-Driven Development with an Agent
- Generation of unit tests based on existing source code
- Facilitation of test creation through natural language directives
- Execution of test suites and analysis of failure logs within the platform
- Refinement of assertions following edge-case validation
Navigating Large Codebases
- Automated identification of cross-file references
- Refactoring of shared utilities using guided rename operations
- Synchronized updates to configuration and schema files
- Prevention of context window saturation through targeted prompting
Customizing Copilot for Team Standards
- Definition of repository-specific instructions in .github/copilot-instructions.md
- Enforcement of naming conventions and architectural patterns
- Exclusion of sensitive files and directories from processing contexts
- Creation of standardized prompt templates for recurring tasks
GitHub Copilot Enterprise Governance
- Seat allocation, billing structures, and usage analytics
- Audit logging: tracking generated content versus committed code
- Intellectual property indemnification policies and licensing considerations
- Exclusion of specific file patterns from AI suggestion pipelines
Debugging with Agent Mode
- Collaborative analysis of stack traces with the agent
- Hypothesis-driven debugging: inquiring about test failure causes
- Identification of regression sources using agent-assisted bisection
- Mitigation of hallucination risks when debugging unfamiliar code
Performance and Limit Management
- Comprehension of daily request limits and model quotas
- Optimization of prompt length to prevent response truncation
- Model switching strategies for diverse task requirements
- Monitoring of agent latency and implementation of caching strategies
Security and Compliance for Enterprises
- Data handling protocols: distinguishing between external processing and local retention
- Prevention of secret and credential leakage via prompt inputs
- Alignment with GDPR, SOC 2, and FedRAMP regulatory requirements
- Security assessment of generated code for injection vulnerabilities
Troubleshooting Common Scenarios
- Diagnosis of instances where codebase context is overlooked
- Resolution of indexing failures in large-scale repositories
- Management of rate limit errors during peak usage periods
- Remediation of IDE extension synchronization issues
Summary and Future Roadmap
- Review of Agent Mode capabilities and practical application workflows
- Outlook on GitHub Copilot roadmap and forthcoming agent features
- Access to resources for maintaining currency with Copilot releases
Requirements
- Proficiency in object-oriented or functional programming paradigms
- Valid GitHub account and foundational knowledge of Git workflows
- Familiarity with at least one integrated development environment (VS Code, JetBrains, or Neovim)
Audience
- Developers currently utilizing Copilot who seek to enable advanced agent capabilities
- Engineering managers overseeing the deployment of Copilot across development teams
- Security teams evaluating policies for AI-assisted code generation
21 Hours
