Agentic retrieval techniques: a complete guide

Agentic retrieval is an advanced architectural pattern where a large language model (LLM)-powered agent dynamically controls the retrieval process, iterating and adapting its queries until satisfactory results are obtained, unlike traditional retrieval-augmented generation (RAG) which follows a static pipeline. This approach is crucial in modern AI systems where agents must gather evidence across multiple steps, enhance response generation, and refine their search strategies in real-time, often using tools like Redis Iris to integrate memory, live data, and retrieval in a seamless, low-latency manner. Techniques such as hybrid search, multi-level retrieval, reranking, and semantic caching optimize the retrieval process, ensuring that agents not only locate the necessary information but also maintain continuity and context across interactions. Redis Iris plays a pivotal role by serving as a context engine, unifying retrieval, caching, and memory into a singular in-memory platform, thereby supporting the agent's dynamic decision-making and ensuring it has access to fresh, relevant data at every iteration.