Originally published on tamiz.pro.
1. Introduction: The Memory Hype Cycle
AI agent memory has become the latest battleground for vendor differentiation. Whether you're evaluating a vector database, a long-term memory module for an LLM application, or a full cognitive architecture, the marketing claims are strikingly consistent: "infinite context," "perfect recall," and "zero latency." In practice, these claims collapse under the weight of real workloads.
This article is a deep-dive into how to benchmark AI memory systems rigorously and reproducibly. We will move beyond synthetic README benchmarks and build a testing methodology that surfaces the trade-offs you will actually face in production. The focus is on agent memory—the systems that allow a conversational agent to remember prior interactions, user preferences, and long-term facts—but the principles apply to any retrieval-augmented or context-window extension system.
2. What Is Agent Memory, Anyway?
