As AI agents increasingly operate in teams across domains like customer support, research, and autonomous system, the need for shared memory systems becomes essential. These systems allow agents to store, retrieve, and build upon collective knowledge, enabling more coherent, context-aware, and collaborative behavior. This talk explores the core design principles and challenges of building such multi-agent memory systems. We examine various architectural patterns by using hybrid storage models that combine structured and unstructured data. We also distinguish between different types of memory and how agents use them to reason and adapt. A key focus is on how to ensure agents don’t overwrite each other’s updates, how to manage simultaneous reads and writes, and how to maintain a coherent view of memory in dynamic environments. We discuss techniques such as version control, conflict resolution strategies, and locking mechanisms to address these issues. Also, we explore how agents learn from shared memory, how memory can be pruned or decayed over time. We also touch on performance and scalability and propose evaluation metrics for measuring memory effectiveness in multi-agent systems. While the talk will include real-world examples to ground these ideas, the primary focus is on the foundational data engineering and system design challenges that form the backbone of scalable, intelligent agent collaboration.

Explainability and transparency are critical for fostering trust and accountability in autonomous agents, yet achieving these qualities in complex systems presents significant technical hurdles. Panelists will explore the latest advancements in making agent decision-making processes interpretable, discussing methods for ensuring transparency, addressing potential biases, and building systems that provide actionable insights while maintaining high performance and autonomy.