The concept of self-driving chemical laboratories builds on top of two technologies: robust artificial intelligence models that translate synthesis designs into executable instructions, potentially incorporating instrument feedback, and robotic workstations capable of executing those procedures seamlessly and without human intervention. Here we present RoboRXN, a cloud-native framework that unifies transformer-based retrosynthesis and chemical action extraction or prediction models with a commercially available synthesis robot and in-line high-resolution LC–MS. Across more than 3000 actions autonomously executed, we analyzed in details 33 reactions distributed among 10 single-step reactions and 9 multi-step campaigns. Eightythree percent of single-steps and all two-step routes succeeded without parameter adjustment; a branched four-step synthesis completed after one on-deck modification. Regardless of the overall success, we systematically encountered three recurring bottlenecks: hardware rigidity, execution-level data gaps, and slow physical feedback, which are seldom reported in the literature, despite being common to many commercial platform design. We delineate how each failure surfaced, quantify its impact on yield or throughput, and outline engineering and data-centric remedies. By publishing both the successes and the blind spots, we hope to redirect community effort from one-off demonstrations toward robust, transparent, and widely adoptable AI-driven laboratories.