But this is not obvious a priori. More memory does not automatically make an agent better: low-quality traces can teach the wrong lessons, and retrieval gets harder as the store grows. The central question is whether agents can use larger memories productively rather than simply accumulate them.

We've made early steps in this direction at Databricks through ALHF and MemAlign, which adjust agent behavior based on human feedback, and the Instructed Retriever, which enables search agents to translate complex natural-language instructions and knowledge-source schemas into precise, structured search queries. Together, these systems demonstrate that agents can be more helpful through persistent memory. This post presents experimental results demonstrating memory scaling behavior, discusses the infrastructure required to support it in production, and provides a forward looking vision of memory-based agents.

Memory scaling is the property that an agent's performance improves as its external memory grows. Here "memory" refers to a persistent store of information the agent can interact with at inference time, distinct from the model's weights or the current context window.

This makes memory scaling a distinct and complementary axis to both parametric scaling and inference-time scaling, addressing gaps in domain knowledge and grounding that neither model size nor reasoning capability can close on their own.The improvements due to memory scaling are not limited to answer quality. When an agent has memorized the relevant schemas, domain rules, or successful past actions for an environment, it can skip redundant exploration and resolve queries faster. In our experiments, we observe scaling in both accuracy and efficiency.