How do you make an AI agent actually remember?
For detailed breakdown read at sistava.com/en/insights
It is the question that inevitably surfaces once an AI system moves out of prototyping and into long-running production. Why does it forget a core constraint after a week? Why does it re-introduce itself every morning? Why does it pick the wrong tool even though it was corrected three days ago?
At Sistava, where you can hire autonomous AI employees, we had to solve this problem to survive. We run a workforce of around 1,000 AI employees in production, operating continuously across live environments for over two months. At this scale, standard context strategies fail. These systems don't get a polite session reset; they face a massive real-world hurdle: facts change over time.
If a user utilizes Gmail today and switches to Outlook next month, an agent needs to track both. It has to know which one is current, exactly when the switch happened, and it cannot act like the old truth is still valid. Standard vector database similarity scores do not understand chronological decay or truth overrides. Mix old and new context, and the agent confidently fabricates or forgets the one detail that mattered.
