arXiv:2610.30167v1 Announce Type: new Abstract: Optimizing Information Retrieval (IR) pipelines for Retrieval-Augmented Generation (RAG) requires jointly selecting structural components and numerical hyperparameters under limited evaluation budgets. We introduce the Information Retrieval Agentic Harness (IRAH), which combines Language Agent Tree Search for IR (LATS-IR) with offline meta-learned initialization. LATS-IR performs UCT-guided hierarchical search, first exploring macro-structural pipeline choices and then refining numerical parameters, with natural-language reflections to guide subsequent mutations and provide inspectable search traces. We evaluate IRAH on three in-distribution benchmarks and one out of-distribution benchmark to assess generalization under distribution shift. Under the same meta-learned initialization, LATS-IR outperforms TPE on two of three in-distribution benchmarks, with gains of up to 6.4%, and remains competitive on the out-of-distribution benchmark. Across all four benchmarks, warm-started IRAH achieves the highest average held-out nDCG@10 (0.4816) and improves over its cold-start counterpart. These results establish IRAH as a competitive approach to retrieval pipeline optimization under constrained evaluation budgets.