AI memory is really a database problem

"Allie Miller, for example, recently ranked her go-to LLMs for a variety of tasks but noted, "I'm sure it'll change next week." Why? Because one will get faster or come up with enhanced training in a particular area. What won't change, however, is the grounding these LLMs need in high-value enterprise data, which means, of course, that the real trick isn't keeping up with LLM advances, but figuring out how to put memory to use for AI."

"If the LLM is the CPU, as it were, then memory is the hard drive, the context, and the accumulated wisdom that allows an agent to usefully function. If you strip an agent of its memory, it is nothing more than a very expensive random number generator. At the same time, however, infusing memory into these increasingly agentic systems also creates a new, massive attack surface."

Rapid LLM advances make model choice transient, but the need to ground models in high-value enterprise data remains constant. Memory provides persistent context and long-term recall that enables agentic behavior; without memory an agent produces only stochastic outputs. Agent memory differs from LLM parametric weights and ephemeral context windows because it persists across sessions and accumulates knowledge. Embedding memory into agents enlarges the attack surface and raises security, privacy, and governance risks. Organizations must treat agent memory like a database, applying firewalls, strict access privileges, and auditability, rather than a disposable scratchpad or simple SDK feature.

Read at InfoWorld