Self-driving laboratories are increasingly positioned as closed-loop systems that can select experiments, control instruments, interpret measurements, and update subsequent actions with limited direct intervention. In pharmaceutical science, however, technical autonomy does not itself provide the epistemic governance needed to distinguish an informative experiment from a confirmatory test, a repeat measurement from an independent replication, or a recoverable malfunction from a condition requiring human escalation or campaign termination. Without explicit transition and stopping rules, an autonomous platform may efficiently optimize an invalid objective, reinforce bias, repeat contaminated procedures, overinterpret uncertain measurements, or continue experimentation after the scientific value of additional observations has become negligible. This article develops the Pharmaceutical Autonomous Experimentation Governance State Machine as an original conceptual architecture for organizing autonomous laboratory behavior. The proposed construct separates qualified readiness, exploration, confirmation, replication, failure recovery, human escalation, and stopping into distinct governance states linked by evidence-dependent transition guards. It further treats provenance, auditability, uncertainty assessment, and contamination monitoring as continuous control functions rather than retrospective reporting obligations. The central argument is that autonomous laboratory quality cannot be established through a single optimization score, throughput measure, model-confidence estimate, or experimental success indicator. Evaluation must instead examine whether the system enters the correct scientific state, preserves the distinction between provisional and confirmed evidence, detects failure, escalates unresolved uncertainty, and stops under success, futility, risk, contamination, or resource constraints. The architecture is not presented as an empirically validated controller, regulatory framework, or deployment-ready standard. Its value lies in specifying testable governance relationships and boundary conditions for future pharmaceutical laboratory research.