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

About this episode

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, decision-trace research, and richer time and transformation mechanisms. Additionally, Vas touches on policy guardrails for agent actions and the potential for more efficient "pseudo-languages" for multi-agent collaboration.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I'm interviewing Vasilije Markovic about agentic memory architectures and applications

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving an overview of the different elements of "memory" in an agentic context?
• storage and retrieval mechanisms
• how to model memories
• how does that change as you go from short-term to long-term?
• managing scope and retrieval triggers
• What are some of the useful triggers in an agent architecture to identify whether/when/what to create a new memory?
• How do things change as you try to build a shared corpus of memory across agents?
• What are the most interesting, innovative, or unexpected ways that you have seen agentic memory used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cognee?
• When is a dedicated memory layer the wrong choice?
• What do you have planned for the future of Cognee?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

• Cognee
• AI Engineering Podcast Episode
• [Kimball Memory](
• Cognitive Science
• Context Window
• RAG == Retrieval Augmented Generation
• Memory Types
• Redis Vector Store
• Qdrant
• Vector on Edge
• Milvus
• LanceDB
• KuzuDB
• Neo4J
• Mem0
• Zepp Graphiti
• A2A (Agent-to-Agent) Protocol
• Snowplow
• Reinforcement Learning
• Model Finetuning
• OpenClaw

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA