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Presented by: Oracle, LangChain & MLOps Community
Oracle provides the infrastructure to build production-ready AI systems, combining structured data, vector search, & scalable database capabilities in Oracle AI Database.
Together with the MLOps Community, this workshop is focused on practical patterns for building real-world AI systems. No hype. Just technical insight, hands-on learning, & production-minded engineering.
ABOUT THE WORKSHOP
This is not your average AI workshop. It's a hands-on session for engineers who are done building agents that forget everything the moment the session ends.
In this workshop, attendees will build a Python-based AI agent with persistent memory powered by Oracle AI Database. You'll implement a practical memory pipeline - extract store retrieve inject forget - using structured memory & vector-based semantic recall to create agents that can retain useful context across interactions.
Along the way, you'll learn how to encode facts, events, & preferences, design retrieval logic that surfaces the right context at the right time, & apply lightweight forgetting strategies so memory stays useful instead of noisy.
This is a practical, code-focused event built for engineers who want to move beyond demos & build AI systems that are more reliable, adaptive, & production-ready.
AGENDA
Agenda:
5:00 PM - 5:40 PM: Doors Open & Registration
Check-in, refreshments, networking, & technical setup review for the hands-on portions.
5:40 PM - 5:50 PM: Welcome & Opening Remarks
Introduction to the workshop goals, structure, & technical overview of what attendees will build.
5:50 PM - 6:55 PM: Hands-On Workshop - Part 1
Build the foundations of a memory-aware AI agent using Python & Oracle AI Database, including structured memory, storage design, & persistent memory architecture.
6:55 PM - 7:30 PM: Technical Masterclass Keynote Session
Colin Francis from LangChain, will lead a deep dive into Memory Engineering for production AI agents.
7:30 PM - 8:40 PM: Hands-On Workshop - Part 2
Implement semantic recall, retrieval logic, memory injection, & forgetting strategies to make the agent more production-ready.
8:40 PM - 9:00 PM: Wrap-Up, Q&A & Networking
Technical Q&A, networking, & next steps.
What You'll Walk Away With
A working memory-aware AI agent
A reusable notebook & starter repo
Practical patterns for persistent agent memory
A stronger foundation for building production-ready AI systems
WHO SHOULD ATTEND
ML & AI engineers
Backend engineers building AI applications
Technical leads designing agent systems
Builders who want agents that remember, not just respond
Prerequisites: Comfortable with Python & Jupyter notebooks. OpenAI API key (or use provided alternative). Familiarity with LangChain/LlamaIndex helpful but not required.
REGISTER
Seats are limited. Register now to secure your spot.
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