We have observability for every layer of the AI stack except the one that decides what the agent believes

You can debug your prompt. You can swap your model. You can tune your retrieval. But the memory layer underneath all of that is a black box in most products. When something goes wrong, you can't even tell which layer failed and I've been thinking about this for a while now and it keeps bothering me. Some examples of what I mean by "decides what the agent believes": * A user said in January they prefer morning meetings. In April they said afternoons. Which one does your agent surface today, and can you actually inspect why? * A sarcastic comment got stored as a literal preference six months ago. The agent has been acting on it ever since. How would you find this without re-reading every memory in storage? * A derived summary outlived the underlying facts that made it true. The agent still references the summary. Can you trace the where did this memory came from? The frustrating part is that we already know how to build observability for systems. We did it for databases, logs and distributed tracing. So why is the memory layer still a black box? Is it just because the category is young and people are still optimizing for "does it remember things?" Curious what people here think, especially anyone running agents in production. How are you debugging your memory layer right now? Or are you just hoping the retrieval looks right and moving on?