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Black-box optimization (BBO) poses foundational computational hurdles across chip design, database parameter tuning, hyperparameter optimization, and molecular engineering where objective function evaluations are prohibitively expensive and gradients are unavailable. Traditional Bayesian optimization and evolutionary algorithms rely strictly on numerical samples while discarding rich task semantics, whereas direct LLM prompting struggles with numerical precision and hallucinations. Researchers from Nanjing University's National Key Laboratory for Novel Software Technology (LAMDA Group) introduce AgenticBBO-Bench (arXiv:2610.12183), the first unified cross-domain benchmark for evaluating LLM agents in black-box optimization. Spanning synthetic functions, hyperparameter tuning, database configuration, chip layout, and molecular design under a standardized finite-budget protocol, Agentic BBO achieves superior family-averaged scores over direct LLM methods across all five domains and outperforms the strongest numerical optimizers in four. Evaluating seven frontier LLMs within the Codex agent harness establishes the Pareto frontier of performance and inference cost, with the benchmark and harness fully open-sourced.