AI agents can look great in a demo, then fall apart the moment memory, retrieval, and real-world complexity matter.

This meetup is about what it actually takes to build agent systems that can remember the right things, retrieve the right context, and behave more reliably in production.

We'll hear from speakers at Google, Tavily, and Oracle, each sharing practical lessons from building real AI systems, followed by audience Q&A and time to meet others working in the space.

This is a relaxed, down-to-earth local chapter meetup designed for learning, good conversations, and connecting with other engineers and builders in NYC.

TOPICS WE'LL EXPLORE

Some of the themes across the evening will include:

Agent memory and long-term context
Retrieval and knowledge systems
Databases and infrastructure for AI agents
Building agent systems that are more reliable in production
The difference between demo-quality agents and production-ready systems

AGENDA

5:00 - 6:15 PM | Doors Open — Arrival & Networking
Check-in, food, drinks, and informal networking
6:15 - 6:30 PM | Welcome & Kickoff
Opening remarks from MLOps Community and Oracle
6:30 - 7:00 PM | Richmond Alake, Oracle
Memory Engineering and the Rise of Memory-Aware Agents
7:00 - 7:30 PM | Dean Sacoransky, Tavily
Context Management in Research Agents
7:30 - 8:00 PM | George Pearse, Visia
Efficient Context Management for Computer Vision
8:00 - 8:30 PM | Pier Paolo Ippolito, Google
Moving from Prompt to Production: A Standardized Lifecycle for AI Agents
8:30 - 9:30 PM | Open Q&A + Networking
Audience questions, discussion, drinks, and networking

WHO SHOULD ATTEND

Engineers, ML practitioners, and technical builders interested in AI agents, retrieval systems, memory architectures, and real-world LLM infrastructure.

Building AI Agents That Actually Remember