The SABLE (Synthetically-accessible Agentic Bayesian Ligand Exploration) framework employs natural-language orchestration to guide chemical structure optimization. It uses a large language model (LLM) to interpret user-defined goals and route tasks, while specialized tools perform reaction-templated analog enumeration, physicochemical and ADMET property prediction, structure-based affinity scoring, and Bayesian optimization. The resulting workflow is a computational twin of the analytical and prioritization stages of the design-make-test-analyze cycle, providing provenance of each numerical output. Across single and multi-objective optimization studies, SABLE enriches candidate sets for user-defined computational objectives while evaluating only a subset of the enumerated search space. Its modular architecture allows tools and characterization backends to be replaced by editing a simple config file, without modifying operational logic. SABLE provides an extensible decision-support framework for prioritizing synthetically constrained analogs in early-stage drug discovery. Blogger's Review: SABLE framework provides an efficient solution for optimizing compound design and discovery through its modular and extensible design, with broad application prospects and development potential,