LLM-based coding agents are increasingly used in software development, but their use in industrial robot programming remains constrained by vendor-specific APIs, organization-specific code assets, and on-premise deployment requirements. We study a bounded ReAct agent that uses local retrieval over API documentation and code examples to generate collaborative-robot programs from natural-language requirements.

This work is a case study of a structured programming domain in which domain knowledge must remain within the organizational boundary and local models may be sufficient. We evaluate seven open-weight models from 7B to 32B parameters, with five independent runs per model, and include one commercial model as a reference. We also apply the same framework to a smaller public Universal Robots set.

Under our agent and evaluation setup, the strongest local 27B model achieves a mean task success rate of 89.6%. Smaller models often use more steps and tokens because they repeat retrieval or fail to convert retrieved evidence into code. These results suggest that model selection should consider task success, interaction efficiency, and run-to-run consistency.