As codebases grow, AI coding assistants struggle. Context windows overflow, agents lose track of dependencies, and simple text search fails to capture the semantic relationships that define real software. This talk explores three proven architectural patterns that enable AI agents to work effectively with production-scale codebases: semantic code intelligence through Language Server Protocol integration, specialized agent skills via context and tool bundling, and subagent delegation for efficient context management. Through live demonstrations on a popular open-source project like ShadCN, you'll see these techniques tackle the complexity of real-world software—multi-file refactorings,cross-module changes, and dependency tracking that would overwhelm traditional approaches.You'll leave with practical, product-agnostic strategies for building or enhancing AI agents that can handle large codebases with accuracy and efficiency. We'll examine why semantic understanding outperforms text search, how to design focused agent skills that improve task completion, and how parallel subagent architectures prevent context window exhaustion. Whether you're building AI developer tools, architecting multi-agent systems, or contributing to open source, these patterns will help you bridge the gap between toy demos and production-grade AI assistance.
