{"podcast":{"title":"Data Engineering Podcast","slug":"data-engineering-podcast","podcast_index_feed_id":403671,"rss_url":"https://serve.podhome.fm/rss/1c0357c0-6aba-5766-a2d5-2090d8dab6bc","website_url":"https://www.dataengineeringpodcast.com","image_url":"https://assets.podhome.fm/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638557928872209534cover.jpg","author":"Tobias Macey","episode_count":510,"summary":"This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/data-engineering-podcast"},"episode":{"title":"From Data Models to Mind Models: Designing AI Memory at Scale","slug":"from-data-models-to-mind-models-designing-ai-memory-at-scale","published_at":"2026-02-22T23:12:36+00:00","page_url":"https://stenobird.com/podcast/data-engineering-podcast/from-data-models-to-mind-models-designing-ai-memory-at-scale","show_page_url":"https://stenobird.com/podcast/data-engineering-podcast","url":"https://www.dataengineeringpodcast.com/agentic-memory-design-and-application-episode-502","audio_url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/639073981323893202146bcc19-cf93-4fa2-9e96-70bb5d041afc.mp3","summary":"Designing AI memory requires moving beyond simple vector stores to a multi-layered architecture of graph and vector layers. This discussion explores how to implement agentic memory that distinguishes between session and long-term state while maintaining multi-tenant isolation.","meta_description":"Learn how to build scalable agentic memory using graph+vector layers, multi-tenancy, and temporal decay for advanced AI agents.","key_points":["Main idea: Agentic memory must differentiate between short-term session state and long-term persistent knowledge","Practical takeaway: Use a hybrid graph and vector approach to enable complex relationship retrieval and temporal searching","Failure mode: Avoid naive summarization or uncontrolled fine-tuning as substitutes for structured memory systems","Security insight: Implement physical isolation and multi-tenancy to prevent agents from accessing unauthorized data silos","Future trend: The emergence of 'pseudo-languages' or structured SQL-like commands for more efficient multi-agent communication"],"chapters":[{"start_ms":60000,"title":"Introduction to Knowledge Engineering","summary":"Vasilije Markovic discusses his transition from data engineering to cognitive science and the inspiration for building memory engines."},{"start_ms":330000,"title":"Defining Agentic State","summary":"The necessity of providing agents with a state representation to allow for continuity and data exchange across sessions."},{"start_ms":600000,"title":"Long-term vs. Session Memory","summary":"Exploring use cases for persistent memory, such as maintaining user profiles and breaking down data silos."},{"start_ms":1110000,"title":"Optimizing Embedding Retrieval","summary":"Technical considerations for optimizing embeddings and managing large-scale retrieval for agentic workloads."},{"start_ms":1390000,"title":"Temporal Relevance and Decay","summary":"How to model the temporal aspect of memory and handle information that becomes outdated over time."},{"start_ms":1640000,"title":"Advanced Search Architectures","summary":"Implementing multi-modal search across graph and vector stores to find temporal and relational data."},{"start_ms":1890000,"title":"Multi-tenancy and Security","summary":"Architecting isolated memory stores to ensure agents cannot cross-contaminate or access sensitive company data."}],"topics":["Agentic Memory","Vector Databases","Graph Databases","Multi-tenancy","Cognitive Science","AI Infrastructure","Knowledge Engineering","LLM Orchestration"],"duration_seconds":3467,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/data-engineering-podcast/episodes/from-data-models-to-mind-models-designing-ai-memory-at-scale/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/data-engineering-podcast/from-data-models-to-mind-models-designing-ai-memory-at-scale.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}