Redefining Long-Term Memory Ingestion for Streaming Autonomous Agents

Learn how to build a fully asynchronous, streaming-driven memory platform for autonomous and multi-agent systems using Qdrant vector database in this 21-minute conference talk. Discover how to implement durable memory solutions that don't block real-time operations by ingesting conversations and events in real time, then structuring them into comprehensive histories, semantic summaries, and rich metadata. Explore a layered retrieval approach that filters by metadata first, surfaces summaries for context, and deep-links into detailed history only when necessary. Understand how this architecture integrates with popular streaming engines like Kafka, RabbitMQ, and ActiveMQ while emphasizing low latency, backpressure resilience, and scalable retention policies. Master techniques for improving decision quality, reducing prompt bloat, and delivering enterprise-grade responsiveness for continuously operating agents through practical implementation strategies and real-world applications.