Frequently Asked Questions
Common Questions About Multi-Agent Memory and RAG Integration
Everything you need to know before registering.
How do episodic memory and RAG complement each other in a multi-agent system? +
Episodic memory provides continuity: it remembers what happened in past interactions, what decisions were made, and what the user's preferences and context are. RAG provides knowledge: it retrieves relevant domain information for the current query from the embedded knowledge base. In a multi-agent system, the context router combines both: episodic memory provides the conversation and user context, RAG provides the domain knowledge, and together they give each agent a complete, grounded context for accurate and consistent responses.
How do I implement shared RAG access for multiple agents without conflicts? +
Shared RAG access for multiple agents uses a centralized RAG service exposed as an MCP server: all agents invoke the RAG service through typed MCP tool calls rather than accessing the vector store directly. This centralized design handles concurrent access through connection pooling, implements a shared retrieval cache to avoid redundant embedding searches, and maintains consistent citation metadata across all agents that retrieve the same documents. The workshop covers implementing this centralized RAG service pattern.
How do citation chains work when multiple agents access the same knowledge through RAG? +
When agent A retrieves a document through the RAG service and uses a fact in its output, the citation is attached to the output as structured metadata. When agent B receives agent A's output and passes the fact to the RAG service for verification or extension, the original citation travels with the fact. The RAG service's citation manager tracks this provenance chain, so the final output can trace every factual claim back to the original retrieved source regardless of how many agents processed it.
How do I prevent memory and RAG from becoming bottlenecks in a multi-agent system? +
Memory and RAG bottleneck prevention requires three layers: connection pooling that allows multiple concurrent agent queries without blocking, semantic caching that serves frequently retrieved content without repeating the vector store lookup, and asynchronous retrieval that allows agents to begin processing non-knowledge-dependent portions of their task while RAG and memory retrieval run concurrently. The workshop covers implementing all three optimizations in the centralized RAG and memory services.
How do I maintain memory consistency when agents update shared episodic memory concurrently? +
Shared episodic memory consistency uses an optimistic concurrency control pattern: each memory record has a version number, memory updates include the expected version, and the memory store rejects updates where the provided version does not match the current version (indicating a concurrent modification). The orchestrator handles rejected updates by retrying with the latest version after applying the new update on top of the current state. The Glass-Box logging records all memory operations and their version information for consistency auditing.
What is the memory and RAG architecture for a long-running multi-agent system? +
A long-running multi-agent system's memory and RAG architecture must handle: growing episodic memory stores (managed through TTL eviction and importance-based compression), evolving knowledge bases (managed through incremental RAG indexing), shifting user context (managed through memory relevance decay that reduces the weight of old episodic memories over time), and long-running conversation state (managed through session summarisation that compresses multi-session histories into retrievable summaries). The workshop covers each of these long-term management considerations.
