Building AI That Remembers: Architecting Reliable, Context-Aware Enterprise Agents

In this essential course, Kay Malcolm—the vice president of product management at Oracle for the Oracle AI Database—shows you how transitioning from chatbots to memory-enabled agents can vastly improve context retention, reliability, and user satisfaction. Learn about the four types of memory: sensory, working, procedural, and long-term, and how these can be applied to enterprise AI use cases. Dive into practical strategies for integrating data, memory, and tools within an AI infrastructure, focusing on safe governance and compliance measures. Explore high-level architectures that incorporate robust memory systems and ensure data security across workflows. Complete hands-on labs where you will design, build, and scale AI agents capable of maintaining and utilizing memory effectively. Aimed at data leaders, product teams, and IT personnel, this course equips you with the skills to implement memory-enabled agents confidently, ensuring smarter, more efficient enterprise solutions.

Learning objectives
Diagnose common scenarios where AI loses context during real tasks, and troubleshoot how to prevent these breakdowns in your own workflows.
Convert real examples of organizational knowledge—documents, workflows, FAQs—into structured memory elements that AI systems can use in day-to-day operations.
Evaluate AI outputs with and without memory to see how context retention improves accuracy, consistency, and user experience.
Apply lightweight governance steps—such as memory validation, access rules, and update workflows—to keep an AI system’s memory correct and compliant.
Design a practical pilot plan for a memory-enabled AI use case, including selecting a workflow, setting success metrics, and implementing an iterative improvement loop.
Complete a hands-on final project to apply skills and build a memory-enabled agent.