The more I dig into agent memory, the more I am convinced (strong sentiment because of recent bias) that the schema is the real deal here.
Yes, retrieval is important. But to make it more efficient, we should be more prescriptive, define the right schema, and extract the right metadata for hard filtering. That improves precision.
Relying on blunt semantic lookup is a really bad idea.
If you give the model raw text and expect it to figure out the schema that you will ingest into the search engine, you will run into issues like duplications and contradictions. This is a classic data modeling problem.
So, find the verticals in your system, define typed facts for them, and make this a write-time operation instead of a read-time “guess”.
The model extracts the fact. The database decides what that fact means, whether it replaces something, and what the current truth is.
The model is good at extraction and reasoning. It is not reliable at conflict resolution over its own past outputs.
“Deterministic memory” is actually a must-have for agents. The better your data model, the more predictably useful your agent will be.