Large Language Models (LLMs) are powerful, but their limitations become clear in multi-turn interactions: they lose track of context, repeat mistakes, and forget what matters. Lately, developers have relied on context engineering—clever prompt design, retrieval pipelines, and compression—to work around these constraints. But context alone is ephemeral. To build agents that are reliable, believable, and capable, we need to move beyond context and into memory engineering.

This talk introduces memory engineering as the natural progression of context engineering, exploring how to design systems where data is intentionally transformed into persistent, structured memory that agents can learn from, recall, and adapt with over time. We’ll walk through the data→memory pipeline, types of agent memory (short-term, long-term, shared), and practical strategies like reflection, consolidation, and managed forgetting.

Finally, we extend the conversation with a Context Engineering++ perspective—a holistic view of how memory, context, and attention can be engineered together to enable the next generation of agentic systems. Attendees will leave with a clear framework for evolving from prompt engineering to context engineering to memory engineering, and practical guidance on how to architect agents that don’t just respond, but remember, adapt, and grow

Richmond Alake is an AI strategist and engineer specializing in the emerging discipline of memory engineering for AI agents. At MongoDB, he leads initiatives at the intersection of data, developer experience, and generative AI, driving adoption of AI-native architectures and agentic systems.

As the creator of Memorizz, an open-source framework for building memory-augmented AI agents, Richmond has been at the forefront of defining how persistent memory transforms LLMs from stateless chatbots into adaptive, evolving systems. His thought leadership—spanning conference talks, technical cookbooks, and industry publications—focuses on bridging research insights with practical engineering patterns for building reliable, believable, and capable AI.

He has written for leading publications including NVIDIA, Neptune AI, and O’Reilly, authored over 200 articles on AI and developer experience, and collaborated with Andrew Ng on a Retrieval-Augmented Generation (RAG) course for DeepLearning.AI.

Through his work, Richmond helps organizations and developers alike navigate the shift from prompt engineering to context engineering to memory engineering, charting a path toward the next generation of agentic AI.