AI Development Capabilities - The industry is focusing on bringing AI development capabilities through Copilot, starting with code completion and moving towards chat interactions for complex prompts and multi-file changes [1] - Agent mode enables complete task execution with deep interaction, allowing for building apps or refactoring large codebases [2] - Agent mode can interpret readme files, including project structure, environment variable configurations, database schemas, API endpoints, and workflow graphs (even as images), to implement tasks [3][4][5] Model Context Protocol (MCP) - MCP is an open protocol (API for AI) that allows LLMs to connect to external data sources for general or account-specific information [9] - VS Code can be configured to use specific MCPs, allowing Copilot to select the appropriate MCP for a task and connect to it, whether local or remote [11][12] - Developers need to grant permission for Copilot to connect to MCPs, ensuring data access is controlled [20] - GitHub has its own MCP server, enabling actions like committing changes to a new branch and creating pull requests directly from the IDE [26][31] Workflow and Best Practices - Copilot Instructions, a specially named file, can be used to pre-inject standards and practices into every prompt, such as code style guidelines and security checks [28][29][30] - Including a change log of everything the agent has done provides a clear record of each step taken [30]