Building an AI Agent that works in a demo with 3 tools is easy. Building one that works with 100+ tools without blowing up your context window or hallucinating is an engineering nightmare.

The secret to stable agents isn't a smarter LLM, it's better retrieval. In this session, we explore OpenSearch as the deterministic "brain" behind autonomous agents.

We will ditch the hype and focus on the architectural patterns for production agents:

The 100-Tool Problem: You cannot stuff every API definition into your system prompt. We will show how to index tool definitions and use vector search to dynamically retrieve only the top 3-5 relevant tools for a specific user query.

Tool Categorization: Not all tools are equal. We will look at how to handle different tool types. From simple Data Retrieval (RAG) to aggregations and Semantic Actions, we will discuss how to model them effectively in your search index.

Hybrid Memory: Finally, we will apply these same retrieval patterns to Long-Term Memory. We will show why semantic search alone fails for recalling user details, and how to use Hybrid Search (BM25 + k-NN) to build a memory system that actually remembers what matters.