AI/ML 
Beginner+ Personalizing AI agents at scale: Inside LinkedIn’s Cognitive Memory Agent
Large language models can reason, but without memory they remain largely stateless, forcing users to repeat context and limiting an agent’s ability to personalize its behavior over time. In this talk, I will share how we built LinkedIn’s Cognitive Memory Agent (CMA), a horizontal cognitive memory platform that enables stateful, context-aware AI agents at scale. I will explain how CMA combines conversational, episodic, semantic, and procedural memory to help agents learn from interactions, understand user preferences, and adapt to how recruiters work.
Using LinkedIn’s Hiring Assistant as a real-world example, I will walk through the architecture behind memory ingestion, hierarchical knowledge representation, and reasoning-based retrieval across multiple memory layers. I will also discuss the production challenges that emerge when memory becomes a first-class component, including latency, evaluation, privacy, access control, stale or conflicting information, and maintaining user trust.