Richmond Alake

Every firm can buy the same frontier models. What separates one firm's agents from another's is what they know about the business, what they remember, and whether they improve. In financial services that is also where entitlements, audit and model risk sit, which is why so many agents stall between pilot and production.

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Inspired by the complexity of human memory systems—such as episodic, working, semantic, and procedural memory—this talk unpacks how AI agents can achieve believability, reliability, and capability by retaining and reasoning over past experiences.

Artificial Intelligence thrives on data—especially timely data. In this talk, we’ll explore how to integrate event-driven architectures with popular AI/ML frameworks to unlock real-time intelligence. We’ll dive into the nuts and bolts of constructing a continuous data pipeline using open-source technologies like Kafka Streams, Apache Flink, and popular AI libraries such as TensorFlow or PyTorch. We’ll walk through end-to-end examples: from data ingestion, cleaning, and feature extraction, to model inference in near-real time. You’ll discover how to optimize model performance under streaming conditions, employing sliding windows and advanced time-series techniques. Additionally, we’ll address operational challenges such as model updates in production, handling concept drift, and balancing compute resources with streaming throughput demands. Attendees will leave with a blueprint for setting up an event-driven AI pipeline, armed with concrete tips on choosing the right open-source frameworks, monitoring streaming model performance, and orchestrating seamless model deployments. If you’ve ever wondered how to blend AI with real-time event processing to deliver actionable insights the…