Large Language Models have demonstrated a remarkable capability in natural language and program generation and software development. However, the source code generated by the LLMs does not always meet quality requirements and may fail to compile. Therefore, many studies evolve into agents that can reason about the problem before generating the source code for the solution. The goal of this paper is to study the degree to which such agents benefit from access to software development tools, in our case, a gcc compiler. We conduct a computational experiment on the RosettaCode dataset, on 699 programming tasks in C. We evaluate how the integration with a compiler shifts the role of the language model from a passive generator to an active agent capable of iteratively developing runnable programs based on feedback from the compiler. We evaluated 16 language models with sizes ranging from small (135 million) to medium (3 billion) and large (70 billion). Our results show that access to a compiler improved the compilation success by 5.3 to 79.4 percentage units in compilation without affecting the semantics of the generated program. Syntax errors dropped by 75%, and errors related to undefined references dropped by 87% for the tasks where the agents outperformed the baselines. We also observed that in some cases, smaller models with a compiler outperform larger models with a compiler. We conclude that it is essential for LLMs to have access to software engineering tools to enhance their performance and reduce the need for large models in software engineering, such as reducing our energy footprint.