Software developers increasingly use AI agents that execute multi-step programming tasks on their own. However, it is unclear if and how this transfers to robotic engineering, where engineers program industrial robots in niche, vendor-specific languages and rely on visual feedback from simulation tools. Within this paper, we report a case study on the use of AI agents by an SME that programs industrial robots. Our results show that the agent did not make the engineers more productive, with the exception of a task that required the generation of highly repetitive code. However, the results with the agent improved over time, as the engineers learned how to use it and the agent was adapted to the domain. Moreover, the experience with AI mattered more for the success with the agent than the experience with robotic engineering. This shows that AI agents are not a plug-and-play solution. Instead, companies must train their engineers on the usage of agents, plan time for a learning curve, and ensure that agent configurations match the domain and developer requirements. The strong learning curve we observed further implies that studies on productivity gains with AI should be longitudinal, as they otherwise miss how engineers adopt agents in practice.

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