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Deep Research agents are rapidly emerging as primary users of modern retrieval systems. Yet, existing retrievers remain designed for standalone queries from human users or single-turn Retrieval-Augmented Generation. In contrast, Deep Research is a multi-turn reasoning process, where retrieval should condition on the agent’s evolving reasoning state, rather than the query alone. To address this, we introduce: (1) Reasoning-Aware Retrieval, a retrieval paradigm that jointly embeds the agent's reasoning trace alongside its query; and (2) DR-Synth, a data synthesis method that generates Deep Research retriever training data from standard QA datasets. We demonstrate that both components are independently effective, and their combination yields a trained embedding model, AgentIR-4B, with substantial gains. On the challenging BrowseComp-Plus benchmark, AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch, compared to 52% with conventional embedding models twice its size, and 37% with BM25. Code, data, and model can be found at: https://texttron.github.io/AgentIR/