This session will introduce a new paradigm in AI-powered search where LLM agents orchestrate hybrid retrieval workflows (keyword + vector search) with explainability at the core. Attendees will walk away with practical strategies and a clear roadmap to build trustworthy, high-performance, agentic search systems in OpenSearch.

Key Takeaways from the session will be as follows:
-Current RAG/search solutions are opaque, rigid, and hard to debug.
-Agentic AI + Hybrid Retrieval + Explainability solves these challenges.
-How LLM agents can decide dynamically: BM25, semantic search, or both.
-Orchestrating multi-step RAG pipelines inside OpenSearch.
-Adding explainability layers to trace agent reasoning and retrieval paths.
-Performance tuning with caching, memory, and vector optimization.

Why this session will be unique and should be selected:
First session to unify Agentic RAG + Explainable AI in OpenSearch.
Bridges research (AI agents, XAI) with real-world OpenSearch workflows.
Practical demo + benchmarks showcasing latency, relevance, and cost tradeoffs.