Agent memory is still discussed as a retrieval problem.
“Memory in the Age of AI Agents” paper argues that framing is incomplete.
The paper formalizes agent memory as an evolving state that lives inside the agent loop, not just as an external store to be queried.
Memory is governed by three operators:
1. formation, which decides what gets written;
2. evolution, which determines how memory is consolidated, rewritten, or discarded
3. retrieval, which decides what gets surfaced back into the model at decision time.
Many existing agent memory systems place most of their emphasis on retrieval and rely heavily on static recall mechanisms. Without meaningful evolution, memory keeps growing, due to which redundant entries accumulate, and conflicts remain unresolved.
The result is often a growing record of past interactions rather than a curated and adaptive memory base.
One of the more subtle points in the paper is how it treats short-term versus long-term memory. These are not presented as separate architectural components. Instead, temporal roles emerge from how frequently formation, evolution, and retrieval are invoked.
If formation happens frequently while evolution is rarely applied, the agent becomes verbose but brittle. If evolution is overly aggressive, stability can break down. This perspective helps explain many long-horizon failure modes observed in practice.
The direction suggested is not simply better retrieval. It is the design of memory evolution policies that decide when experience should be compressed, merged, rewritten, or forgotten. This is where memory moves beyond storage and begins to shape agent behavior over time.
