The Current State of Agentic Retrieval - Qdrant Roundtable

Agentic retrieval has evolved significantly, shifting from static RAG systems to dynamic, agent-driven search architectures. Efficient agentic performance requires moving beyond simple retrieval by implementing self-correcting loops and statistical evaluation signals to minimize latency and token costs. Establishing ground truth remains a critical challenge, necessitating iterative, human-guided development rather than relying solely on synthetic data. Memory management—specifically distinguishing between episodic, semantic, and procedural information—is essential to prevent context pollution, with forgetting mechanisms playing a vital role in maintaining relevance. Furthermore, integrating vector search with knowledge graphs provides a robust substrate for handling complex entity relationships. On-device implementations, such as those used in robotics, demonstrate the viability of running lightweight search engines locally, enabling real-time, context-aware interactions without dependence on cloud-based infrastructure.