TL;DR Problem: "Tool overload" is a critical bottleneck for AI agents. Providing an LLM with a large, static list of tools bloats the context window, degrading performance, increasing costs, and reducing accuracy. Solution: Implement a "select, then execute" architectural pattern. Use a lightweight "router" agent to first retrieve a small, relevant subset of tools for a specific task. Then, a more capable "specialist" agent uses that curated set to execute the request. Benefits: Lower latency and cost (fewer tokens), higher tool-selection precision, a scalable architecture for large tool catalogs, and improved reliability. Pattern: This pattern is a form of Retrieval-Augmented Generation (RAG) applied to tools, often called Retrieval-Augmented Tool Selection (RATS). It can be combined with State-Based Gating for even greater precision. How: This post provides a complete, production-aware implementation using Google's Agent Development Kit (ADK).