AI agents with many tools face a dual problem: they pick the wrong tool and waste tokens because tool descriptions get serialized into the context on every call. As agents scale to 50+ tools, errors increase and costs explode.

Semantic tool selection filters tools before they reach the LLM context using vector search, reducing errors and token costs. Join me as I build a live travel agent demo showing how to implement this with minimal Ptython code and production patterns for multi-turn conversations.

AI agents waste tokens sending all tool descriptions on every call and pick wrong tools as they scale. Learn how semantic tool selection reduces errors 75% and token costs 89% using vector search. Live demo with production-ready code you can use today.