In Part 1, we built a Foundry agent with long-term memory that could remember user preferences, roles, and context across sessions without any custom embedding pipelines. If you haven’t read that post, start there. This one builds directly on top of that code. Memory alone is useful. It just doesn’t go very far on its own. Knowing a user prefers bullet-point summaries or works in finance is helpful context, but it doesn’t change what the…
If you’ve worked with AI agents in production, you’ve almost certainly run into this problem: a user tells your agent something important in one session, and the next time they come back, the agent has completely forgotten about it. The user has to repeat themselves. Context is lost. The experience feels disjointed. I ran into this exact issue on a project where users interacted with an enterprise agent multiple times a day. They’d establish context…