When AI agents have dozens of similar tools, they pick the wrong one—and you pay for it. Research
shows tool selection hallucinations increase with tool count, causing agents to call non-existent
functions, choose semantically wrong tools, or bypass tools entirely. In this session, we'll build
a travel agent with 31 tools and use vector-based semantic filtering (FAISS + SentenceTransformers)
to dynamically select only the most relevant tools per query. You'll see how filtering
dramatically improves accuracy while significantly reducing token costs per call. This technique
works with any AI agent framework and scales to hundreds of tools. You'll leave with a reusable
pattern for intelligent tool filtering that makes your agents faster, cheaper, and more reliable.