[Music] Hi everybody, my name is Eno. I really appreciate that introduction. Um, and maybe I can start with a bit of background.Uh, I started working on LLMs about two and a half years ago. uh when uh GBT3.5% was coming out and it became increasingly clear that agentic systems were going to be possible with the help of LLMs. . At factory we believe that the way that we use agents in particular to build software is going to radically change the field of software development. We're transitioning from the era ...

AI Agents and Memory - The presentation focuses on the importance of memory in AI agents, emphasizing that memory is crucial for making agents reflective, interactive, proactive, reactive, and autonomous [6] - The discussion highlights different forms of memory, including short-term, long-term, conversational entity memory, knowledge data store, cache, and working memory [8] - The industry is moving towards AI agents and agentic systems, with a focus on building believable, capable, and reliable agents [1, 21] MongoDB's Role in AI Memory - MongoDB is positioned as a memory provider for agentic systems, offering features needed to turn data into memory and enhance agent capabilities [20, 21, 31] - MongoDB's flexible document data model and retrieval capabilities (graph, vector, text, geospatial query) are highlighted as key advantages for AI memory management [25] - MongoDB acquired Voyage AI to improve AI systems by reducing hallucination through better embedding models and re-rankers [32, 33] - Voyage AI's embedding models and re-rankers will be integrated into MongoDB Atlas to simplify data chunking and retrieval strategies [34] Memory Management and Implementation - Memory management involves generation, storage, retrieval, integration, updating, and forgetting mechanisms [16, 17] - Retrieval Augmented Generation (RAG) is discussed, with MongoDB providing retrieval mechanisms beyond just vector search [18] - The presentation introduces "Memoriz," an open-source library with design patterns for various memory types in AI agents [21, 22, 30] - Different memory types are explored, including persona memory, toolbox memory, conversation memory, workflow memory, episodic memory, long-term memory, and entity memory [23, 25, 26, 27, 29, 30]

I remember lying on the narrow bed of my college town. Eyes fixed on the slow the hypnotical spin of the ceiling fan above me. It was a first year.I hadn't written a single line of working code. I didn't have the clear direction and no one around me had any reason to believe I was building towards something meaningful. And that question kept repeating in my head was what if I go home with nothing. What if I fail publicly and completely.That fear didn't visit occasionally. It moved in. I wasn't just about a ...

I'm Ben Kuss and I'm here to talk about the lessons that we learned at Box building um agent architectures. Um if you don't know Box, uh we are a B2B company. Um many people know us uh from our content sharing, but we think of ourselves as an unstructured data platform.Uh we have uh we typically deal with large enterprises. So like um uh big companies across Fortune 500. We have over 115,000 companies, tens of millions of users and our customers have given us uh entrusted us with over an exabyte of their da ...