The Current State of Agentic Retrieval - Qdrant Roundtable
About this episode
The episode discusses the advancements in agentic retrieval and the challenges of building AI agents that effectively manage information.
Qdrant Roundtable episode: The Current State of Agentic Retrieval Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide Big shout-out to Qdrant for the collaboration! // 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…
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