Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring a pipeline is an expensive hyperparameter optimization problem over many interacting choices, from chunking and embedding model to reranking and generation. Existing optimizers, from greedy search to Bayesian optimization, reduce each trial to an aggregate score and search without modeling why a configuration performed as it did, even though the retrieved chunks already provide evidence about whether each failure occurred during retrieval or after it. We introduce Agentic AutoRAG, an LLM-agent optimizer for multi-objective RAG hyperparameter...
| Authors | Lasse B. Strand, Robert Jakob, Kevin O'Sullivan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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