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Qdrant Roundtable episode: The Current State of Agentic Retrieval
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// Abstract
AI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures.
As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time.
If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems.
// Bio
Ewa Szyszka
Ewa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work.
Dylan Couzon
Dylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what heβs building and learning along the way.
Neil Kanungo
Neil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan.
Evgeniya Sukhodolskaya
Developer Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Masterβs in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies.
Andrei Cristea
Andrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Masterβs degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering.
Hosted by Demetrios
// Related Links
Website: https://qdrant.tech/
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