Outbound agents cold-start on every interaction. They open the same way, ask what they already asked, and forget the call the moment it ends. dee4dee is an outbound agent that doesn't. It finds one specific person from their public work, emails them, and calls them — and every conversation it has becomes memory in MongoDB Atlas, so the next one starts further along. Research writes what it finds as episodes: one fact, one Voyage voyage-4 vector, stored in Atlas. Mid-call, while a human is waiting to be answered, "which project of mine did you find?" is a $vectorSearch over that person's episodes, reranked with rerank-2.5 and spoken back in about 500ms. "Send me a visual of how I'd use this" generates a picture from their own record and emails it as an attachment before they hang up. "Thursday at two" creates a real Google Calendar event with a Meet link. Then the loop closes: the meeting transcript is written back as an episode, and the next conversation retrieves it by meaning. Asked "what is his team stuck on" — a question only that call could answer — the call transcript comes back ranked first at 0.86, against 0.33 for everything known beforehand. Persons and episodes join on one key, so a call, an email reply and a meeting land on a single record instead of forking into three. The embedding cache is its own collection rather than process state, so a warm-up in one process is a cache hit in another and survives restarts. Every arrow in the architecture diagram is a real read or write against a live Atlas M10.
Team
Placement
Participant
