Building Reliable Compound AI and Agentic Systems With Memory скачать в хорошем качестве

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 by Richmond Alake, Developer Advocate (AI/ML), Mongo DB 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.