AI Agents Memory Systems + Gemini Embedding 2 Rich Context Simplified RAG Search
Details
Welcome back to our meetup in June.
- 06:00 PM Registration, dinner, networking
- 06:30 PM Talk starts
Talk 1:
Practical AI from Gemini Embedding 2 for Simplified RAG Search with Rich Context and Less Preprocessing - Imagine the possibilities if you could find a document using an image, or find audio from a report, or find a video with a sentence.
- Simplifies RAG Systems (one multimodal retrieval pipeline instead of multiple fragmented systems)
- Less preprocessing (e.g. no transcription needed) less infrastructure complexity
- Richer context (text + visuals + audio)
- A major step from “document chatbots” to truly context-aware AI systems
Gemini Embedding 2 changes what Retrieval-Augmented Generation (RAG) can fundamentally do.
Instead of treating text, images, audio, video, and documents as separate systems, AI can now retrieve and reason across all of them together in one shared understanding layer.
This means users can search videos with natural language, retrieve diagrams from spoken conversations, match screenshots to documentation, or ground AI agents with real-world multimodal context — capabilities that were previously complex or unreliable without multiple specialized pipelines.
Talk 2:
Designing Memory Systems for AI Agents
AI agents need memory to maintain context across sessions, learn from experience, and handle long-running tasks. The challenge? Deciding what to remember, where to store it, and how to retrieve it when it matters. In this workshop, you'll learn a practical framework for architecting memory systems that actually work in production. We'll cover:
- Types of memory in agentic systems
- Storage patterns: Where to persist memories and how to structure them for retrieval
- Retrieval strategies: Combining vector search with metadata, recency, and other signals
- Memory lifecycle: When to create, update, or prune memories to keep your system performant
You'll apply this framework by building memory into an AI agent and seeing how different design choices impact behavior.
You will be provided with all the resources required to successfully execute the hands-on portions of the workshop, including Jupyter Notebook templates with pseudocode. During the workshop, you will replace the pseudocode in the templates with your code.
At the end of the workshop, you will also be able to check your learning and earn a skill badge to share with your network! https://learn.mongodb.com/courses/memory-for-ai-applications
## Future Talk: TBA
Awaiting confirmation: Federated Layer to Manage a Multi-Agent Synthetic Workforce
Agents are starting to act as workers in real production systems, and workers need a management layer. The question everyone should take back to your laptop: in my own agent stack, what happens on the second concurrent write, and can I prove, today, under whose authority my agent acted? How do I manage my synthetic workforce in production.