Beyond Vector Search: Building an Adaptive Retrieval Router for Agentic AI Systems

Author(s): Abi Originally published on Towards AI. A hands-on guide to making retrieval a learnable decision layer—with code, architecture, and production trade-offs. Vector search works great for “one query, one answer” workflows. But agentic AI systems retrieve multiple times across a plan — and a small miss early becomes a compounding error that derails the entire task. Adaptive Retrieval Router Architecture: Query → Router (extracts features, scores strategies) → Retriever (keyword/vector/hybrid) → Evaluator → Feedback Loop → TelemetryThe article discusses the need for an adaptive retrieval system for agentic AI, highlighting the problems with static retrieval in dynamic workflows and illustrating the design of an adaptive retrieval router that improves decision-making through feedback loops, thereby addressing compounding errors and enhancing performance in retrieval tasks. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI