Solid-form screening is a foundational step in pharmaceutical development because the physical form of an active pharmaceutical ingredient can influence stability, manufacturability, dissolution, and downstream formulation strategy. Polymorphs, cocrystals, and salts each expand the developable form landscape, but they also increase the complexity of experimental exploration. Current solid-form screening workflows often rely on expert-designed experimental grids, manual interpretation of characterization data, and sequential decision-making. These practices can be slow, material-intensive, and vulnerable to incomplete exploration of crystallization conditions. This article proposes an autonomous AI planning agent that designs solid-form screening protocols, coordinates robotic execution, analyzes diffraction and spectroscopic data in real time, and adaptively refines the next experimental campaign. The agent is conceptual and intended as a system architecture rather than a report of experimental performance. The proposed system combines a Bayesian or reasoning-based planning core, robotic crystallization and sample-handling modules, solid-state characterization tools, and AI-powered form identification. A learning loop updates the agent’s internal representation of the crystallization space after each experimental batch. Such an agent would be expected to make solid-form screening more systematic, traceable, and adaptive. It could support broader exploration of polymorph, cocrystal, and salt landscapes while preserving expert oversight at critical scientific and safety decision points. An autonomous solid-state screening agent could transform solid-form selection from a predominantly empirical workflow into a data-driven, closed-loop planning process. Its value would depend on robust integration of planning, robotics, characterization, human review, and prospective validation.