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Richmond Alake
Developer Advocate (AI/ML), Mongo DB
Richmond Alake is an AI/ML Developer Advocate at MongoDB, where he creates high-quality technical learning content for Developers and MongoDB customers building AI applications.
In this role, he provides expert guidance on best practices for developing AI solutions that leverage Large Language Models (LLMs) and MongoDB, as well as offering insights on integrations and other critical aspects of AI development. Prior to joining MongoDB, Richmond served as a Machine Learning Architect at Slalom Build within their Data Engineering practice. There, he explored the use of Generative AI solutions for a diverse range of use cases as well as implementing efficient compute data pipelines for AI/data intensive applications. Richmond's expertise in AI extends to his previous role as a Computer Vision Engineer at a London-based startup. In this position, he worked with state-of-the-art deep learning models to create interactive and immersive experiences on mobile devices.
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A.I. Summit
March, 13, 2025
16:15 -
17:15 EEST
Advancing your Strategy by Integrating AI into your Sytems
Building Reliable Compound AI and Agentic Systems With Memory
Agentic Systems and extensible compound AI systems are revolutionizing LLM applications, positioning themselves as critical tools in modern AI development. These advanced systems go beyond traditional automation, offering capabilities that drive significant productivity and efficiency gains in enterprise and commercial workflows. However, adopting AI Agents and Agentic Systems at scale poses unique challenges, particularly in ensuring consistent performance, reliability, and scalability.
Central to overcoming these challenges is the role of memory. Memory within AI systems is not only essential for retaining operational data but also for enabling adaptive learning, entity profiling, and customized interactions. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent’s functionality. This talk will delve into the architecture of Agentic Systems and examine how various forms of memory—working memory, data stores, profilers, and toolboxes—contribute to creating robust, efficient, and scalable AI solutions. Attendees will gain insight into how memory is leveraged to enable learning from past executions, personalize interactions, and enhance system capabilities in complex AI applications.
Central to overcoming these challenges is the role of memory. Memory within AI systems is not only essential for retaining operational data but also for enabling adaptive learning, entity profiling, and customized interactions. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent’s functionality. This talk will delve into the architecture of Agentic Systems and examine how various forms of memory—working memory, data stores, profilers, and toolboxes—contribute to creating robust, efficient, and scalable AI solutions. Attendees will gain insight into how memory is leveraged to enable learning from past executions, personalize interactions, and enhance system capabilities in complex AI applications.
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