Artificial intelligence systems are moving from retrospective pharmaceutical prediction toward operational roles in which they formulate plans, invoke computational tools, rank candidate experiments, and coordinate laboratory actions. This transition creates an unresolved accountability problem: an agent may be causally influential in selecting an experiment without possessing the professional authority, institutional duties, or capacity for answerability normally associated with scientific decision-making. This critical review evaluates how accountability should be structured when an artificial intelligence agent participates in choosing the next pharmaceutical experiment. The literature discussed in this critical review was organized around pharmaceutical evidence quality, autonomous chemical experimentation, algorithmic accountability, operational ethics, and human control. The review distinguishes operational agency from decision authority, answerability, and responsibility for remedy. It argues that accountability cannot be assigned solely to the scientist who approves an experiment, the developer who created the model, the organization that deployed the system, or the agent that generated the recommendation. Instead, accountability must be distributed across the full decision lifecycle while preserving identifiable non-delegable duties. The central conceptual contribution is a proposed accountability lens that evaluates agent-selected experiments according to evidence provenance, data suitability, tool validity, uncertainty, feasibility, safety, authorization, traceability, override capacity, and contestability. Existing evidence supports bounded capabilities in planning, robotic execution, and chemistry-tool orchestration, but does not establish routine readiness for autonomous pharmaceutical experiment authorization. Important limitations include heterogeneous validation settings, bespoke laboratory systems, weak prospective comparison, and limited evidence connecting explanations or audit records to improved scientific outcomes. Agentic pharmaceutical science should therefore be governed as a socio-technical experimental system rather than as an autonomous model operating outside established scientific responsibility.