Frequently Asked Questions
Common Questions About RAG Pipelines for Multi-Agent Systems
Everything you need to know before registering.
What is different about a RAG pipeline designed for multi-agent systems? +
A multi-agent RAG pipeline differs from chatbot RAG in four key ways: it handles concurrent agent queries with connection pooling and caching, it tracks citation chains through agent handoffs so downstream agents know the original source of any claim, it provides a shared episodic memory store that multiple agents can read and write, and it exposes all retrieval capabilities as MCP-typed services that agents can invoke through the standard protocol.
How does citation tracking work in a multi-agent RAG pipeline? +
When agent A retrieves a document and uses it in its output, that citation is attached to the output as structured metadata. When agent B processes agent A's output, the citation metadata propagates with the claimed fact. This citation chain means the final output of the multi-agent system can trace every factual claim back to the original retrieved source, regardless of how many agents processed it along the way.
How do multiple agents share a RAG knowledge base without conflicts? +
Shared RAG access in a multi-agent system uses connection pooling to handle concurrent queries, a read-through cache to avoid redundant embedding searches for similar queries, and distributed locking for any write operations to the knowledge base. The workshop covers the connection management and caching architecture that makes shared RAG access reliable and performant under concurrent agent load.
How do I expose my RAG pipeline as an MCP service? +
Exposing RAG as an MCP service means defining an MCP server with retrieval tools (query the knowledge base, retrieve by ID), resource endpoints (access specific documents), and prompt templates (structure retrieval results for agent consumption). The workshop covers the complete MCP RAG server implementation that lets any agent in the system invoke retrieval through the standard protocol.
What caching strategies work for a multi-agent RAG pipeline? +
The workshop covers three caching strategies for multi-agent RAG: query result caching (storing retrieval results for identical queries), embedding caching (pre-computing embeddings for frequently accessed documents), and semantic caching (storing results for semantically similar queries even if the exact text differs). Each caching layer reduces latency and improves consistency for concurrent agent access.
How do I keep a multi-agent RAG knowledge base current without disrupting running agents? +
Keeping the knowledge base current while agents are running requires an incremental indexing strategy that adds new content to the vector store without requiring a full re-index. The workshop covers implementing a document change monitoring pipeline that detects updated content, re-embeds only changed documents, and updates the index while maintaining read availability for agents that are actively querying.
