Scout's 10M-token window enables single-pass synthesis: retrieve all documents relevant to all aspects of query, Scout processes cross-document reasoning without re-querying Zilliz.
Traditional RAG struggles with multi-hop queries: "Find vendors in contracts who also appear in compliance reports." This requires: (1) retrieve contracts → extract vendors → forget contracts, (2) retrieve compliance → search for vendors → forget results, (3) imprecise matching. Scout eliminates the forgetting: retrieve all contracts AND compliance reports in a single Zilliz query, Scout processes both simultaneously, maintaining cross-document connections throughout. The 10M-token window is large enough that all source material stays in-context.
With Zilliz Cloud, this changes strategy. Instead of sequential retrieval (retrieve, process, re-retrieve), use comprehensive retrieval (return all vectors matching multi-aspect query). Zilliz Cloud can filter with multiple metadata criteria, returning 500–1000 relevant vectors. Scout synthesizes all 500 in one pass, solving the multi-hop problem directly. This is why Scout adoption spiked in April 2026 for agentic RAG—it supports reasoning that previously required agentic loops and iterative retrieval.
Related Resources
- Agentic RAG with Claude and Milvus — agentic patterns with long-context models
- Retrieval-Augmented Generation (RAG) — multi-hop query architecture
- Getting Started with LlamaIndex — query composition strategies