Frequently Asked Questions
Common Questions About RAG for Multi-Agent AI Systems
Everything you need to know before registering.
How is retrieval-augmented generation different in a multi-agent system? +
In a multi-agent system, retrieval-augmented generation must handle: concurrent queries from multiple agents without conflicts, citation metadata that propagates through agent-to-agent handoffs preserving source attribution, shared episodic memory that multiple agents can read and contribute to, retrieval confidence information that informs downstream agent routing decisions, and exposure through a typed MCP interface so retrieval can be invoked by any agent in the system.
How do citation chains work in multi-agent retrieval-augmented generation? +
Citation chains in multi-agent RAG work by attaching structured citation objects to every retrieved fact as it moves through the agent system. When agent A retrieves a document and uses a fact in its output, the citation metadata (document ID, section, confidence score) is attached to that fact. When agent B processes agent A's output, the citation metadata travels with the fact. The final synthesis agent can then verify that every claim in the system's output traces to an original retrieved source.
What is the role of retrieval-augmented generation in the Glass-Box Context Engine? +
RAG serves as the knowledge grounding layer of the Glass-Box Context Engine. The context engine constructs semantic blueprints for each agent that include a knowledge section populated by RAG retrievals. The Glass-Box logging layer captures every retrieval operation, which makes the knowledge basis of every agent decision observable and auditable. Without RAG, agent decisions cannot be traced to specific knowledge sources.
How do I prevent retrieval-augmented generation from becoming a bottleneck in a multi-agent system? +
Preventing RAG from becoming a bottleneck requires three optimizations: connection pooling so multiple agents can query the vector store concurrently without blocking each other, a semantic cache that serves results for similar queries without re-executing the embedding search, and async retrieval that allows agents to begin processing while retrieval is still running for the portions of the response that do not require knowledge grounding.
Can retrieval-augmented generation work offline in a multi-agent system? +
Yes. Once the knowledge base is embedded and stored in the vector store, retrieval-augmented generation works completely offline. The embedding computations happen once during indexing (which requires access to an embedding model). Retrieval at query time is purely a vector similarity search against the pre-built index. The workshop covers setting up a fully offline RAG pipeline that requires no external API calls during operation.
How do I keep the retrieval-augmented generation knowledge base synchronized with changing source documents? +
Knowledge base synchronization uses a document change monitoring pipeline that detects when source documents are modified, re-computes embeddings for changed sections, updates the vector store index incrementally, and invalidates any episodic memory entries that referenced the changed content. The workshop covers implementing this synchronization pipeline so your RAG knowledge base stays current without requiring full re-indexing when source documents change.
