RAG Expands AI Agent Memory With 3 External Storage Types
Updated
Updated · InfoWorld · Jul 1
RAG Expands AI Agent Memory With 3 External Storage Types
3 articles · Updated · InfoWorld · Jul 1
Summary
AI agents perform better when long-term context is moved out of an LLM’s limited context window and retrieved on demand, reducing glitches, stalls and nonsensical output.
RAG splits memory into short-term working context and persistent storage, letting agents keep immediate conversation data in-window while pulling older or broader information only when needed.
Three storage types underpin that setup: episodic memory for past decisions and outcomes, semantic memory for facts and preferences, and procedural memory for reusable task steps and skills.
Vector databases often power the storage layer, but deployments vary from server-side services to local systems, with trade-offs in storage, processing power and maintenance.
Shared RAG stores can support multiple agents, though each should keep separate contexts to avoid interference; tools such as Microsoft AutoGen can build more controlled multi-agent setups.