Bayesian Optimization (BO) is widely used to optimize expensive black-box objectives by learning a probabilistic surrogate and querying new points via an acquisition function. Classic approaches, however, tightly bind acquisition behavior to the surrogate (often a GP) and a small set of fixed statistics, which can make exploration–exploitation tradeoffs inflexible and sensitive to hyperparameters. These limitations become acute in higher-dimensional or irregular landscapes relevant to engineering design. We propose an agentic LLM framework that shifts the locus of intelligence from a static, model-tied acquisition rule to a problem-adaptive acquisition designer implemented with one or more large language models. Prior LLM-for-optimization efforts have largely focused on direct candidate generation (or meta-prompted solution search), leaving the acquisition design step to conventional surrogates; by contrast, we use LLM agents to recommend task-appropriate metrics and weights and to compose an acquisition function aligned with user criteria.