From Data Models to Mind Models: Designing AI Memory at Scale

About this episode

Vasilije Markovich discusses the design and implications of agentic memory in AI systems.

Summary  In this episode of the Data Engineering Podcast, Vasilije "Vas" Markovich, founder of Cognee, discusses building agentic memory, a crucial aspect of artificial intelligence that enables systems to learn, adapt, and retain knowledge over time. He explains the concept of agentic memory, highlighting the importance of distinguishing between permanent and session memory, graph+vector layers, latency trade-offs, and multi-tenant isolation to ensure safe knowledge sharing or protection. The conversation covers practical considerations such as storage choices (Redis, Qdrant, LanceDB, Neo4j), metadata design, temporal relevance and decay, and emerging research areas like trace-based scoring and reinforcement learning for improving retrieval. Vas shares real-world examples of agentic memory in action, including applications in pharma hypothesis discovery, logistics control towers, and cybersecurity feeds, as well as scenarios where simpler approaches may suffice. He also offers guidance on when to add memory, pitfalls to avoid (naive summarization, uncontrolled fine-tuning), human-in-the-loop realities, and Cognee's future plans: revamped session/long-term stores…

People in this episode

Host: Tobias Macey

Guest: Vasilije "Vas" Markovich

Topics covered

• AI memory
• agentic memory
• knowledge retention
• data storage
• machine learning
• real-world applications

Keywords

• agentic memory
• knowledge sharing
• multi-tenant isolation
• latency trade-offs
• metadata design
• reinforcement learning
• session memory
• permanent memory

Mentioned in this episode

Organizations: Cognee

Products: Redis, Qdrant, LanceDB, Neo4j

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