AI agents are only as good as the information they can find, retrieve, and remember.

Join us for a special community discussion with the Qdrant team as we explore the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures. As agents evolve beyond simple chatbots into autonomous systems capable of reasoning across large amounts of information, retrieval has become one of the most critical layers in the AI stack.

Together, we'll dive into the practical challenges of building systems that can remember what matters, forget what doesn't, and consistently retrieve the right context at the right moment.

What we'll explore

🧠 Agentic Memory Architectures

Why memory is becoming a core component of production AI systems
Managing long-term memory without bloating context windows
Memory lifecycle management: what agents should remember, and what they should forget
Practical patterns for persistent memory using vector databases

🔍 Late Chunking & Production RAG

Why traditional chunking strategies often fail
Preserving document-level context and meaning
Improving retrieval quality and relevance in real-world applications

📊 Evaluating Search Quality

Measuring retrieval performance beyond simple accuracy metrics
Semantic search vs exact-match retrieval
Practical frameworks for evaluating search systems in production

🌐 Embeddings Beyond Text

Retrieval for code, images, tables, time-series, and multimodal data
Emerging patterns for next-generation AI applications
Real-world examples from production deployments

You’ll hear from an exceptional lineup of experts from Qdrant, including Neil Kanungo, Head of Developer Relations, alongside Ewa Szyszka and Dylan Couzon, both Developer Relations Engineers, and Evgeniya Sukhodolskaya, Senior Developer Advocate. Together, they bring deep experience in vector search, retrieval systems, RAG architectures, and production AI applications, offering practical insights from helping developers and organizations build scalable AI systems powered by semantic search and agentic workflows. Hosted by Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community, this session promises an engaging mix of technical expertise, real-world lessons, and interactive discussion.

Expect practical insights, real-world examples, audience Q&A, and plenty of opportunities to engage directly with the engineers helping shape the future of vector search and AI retrieval systems.

Whether you're building RAG applications, AI agents, search systems, or knowledge platforms, you'll leave with actionable ideas you can apply immediately.

Because when AI systems fail, it's often not because they can't think.

It's because they can't find what they need to know.

The Current State of Agentic Retrieval