context-engineering

Optimize AI agent context by curating and managing the information provided to the agent. Structuring context with rules files, specs, and source files ensures the agent has the right information at the right time to maintain output quality.

Structure project context by organizing information from persistent to transient layers. Providing rules files, specs, source files, and error outputs sets up the context for a new coding session while maintaining focus and avoiding overload.

Model Context Protocol integration works by connecting to MCP servers to provide richer context. This allows AI agents to access additional context sources such as documentation, browser states, and project memory during a coding session.

AI agent confusion often stems from ambiguity and missing requirements in the provided context. Explicit confusion management techniques surface these missing requirements to ensure the agent works accurately and efficiently.

Yes, context engineering handles both transient and persistent project specs by organizing them into a context hierarchy. This maintains agent focus and prevents context overload throughout the coding session.

When missing requirements cause an AI agent to stall, apply explicit confusion management techniques to surface the ambiguity. Curating the provided context with the necessary specs and error outputs resolves the stall and ensures accurate work.