What is Read-Only Memory?
Read-only memory in AI agent systems refers to static knowledge sources that agents can retrieve information from but cannot modify during operation. This typically includes pre-loaded knowledge bases, document collections, or reference materials that provide factual information, procedural knowledge, or domain expertise. Unlike dynamic memory that agents build through interaction, read-only memory represents fixed knowledge that remains constant across sessions.
Read-only memory is commonly implemented through RAG systems where a knowledge base of documents is embedded and stored in a vector database before the agent begins operating. The agent can search and retrieve from this knowledge but doesn't add new documents or modify existing ones. This separation between static knowledge and dynamic memory provides several benefits: it ensures factual information doesn't drift or become corrupted, allows multiple agent instances to share the same knowledge base, and simplifies system design by separating concerns.
Many production agent systems use read-only memory for their primary knowledge source (company documentation, product manuals, policy documents) while maintaining separate writable memory for conversation history, user preferences, or learned experiences. This hybrid approach provides agents with both authoritative reference information and the ability to personalize and adapt based on interactions. The distinction between read-only and writable memory is an important architectural consideration that affects system reliability, knowledge governance, and scaling characteristics.