A major pain point for our is that they "forget" what happened in previous sessions. For a customer support use case, this is a deal-breaker. In 2026, what is the standard for implementing persistent, long-term memory? Are people just dumping everything into a vector database, or is there a more structured way to help agents remember user preferences and past interactions across different days?
3 answers
In 2026, "dumping everything into a vector DB" is considered a legacy approach because it leads to too much noise. The modern production stack for uses a three-layer memory architecture. First, we have the "Session Transcript" (JSONL) for immediate context. Second, we use a "Profile/State" layer in a traditional relational database (like Postgres) to store hard facts and user preferences. Third, we have a "Semantic Summary" layer where the agent periodically writes its own "memos" about a user. This allows the agent to load only the most relevant "memories" based on the current intent, preventing context drift and keeping costs low.
How do you handle the "memory cleanup" though? If the keeps writing memos about a user, won't that summary eventually become too bloated and start confusing the model again?
We found that giving the a simple MEMORY.md file that it can read and write to is actually more effective than complex vector search for many tasks.
That's a great "keep it simple" tip, Sharon. We use a similar approach where the agent updates a "state" JSON at the end of every interaction. It’s deterministic, easy to audit, and very reliable.
We use a "compaction" process similar to how databases handle logs. Once a week, a background task takes all the small memos and synthesizes them into a single, high-level "User Persona" document. This ensures that the only sees the most updated and distilled version of the user's history, rather than a thousand conflicting snippets from six months ago.