An analysis was conducted to compare the performance of language model-powered agents, particularly focusing on the complexity of their interaction techniques in executing data tasks. The study assessed the efficacy of ReAct agents, which employ iterative reasoning, against simpler routing agents in handling financial queries over Uber's quarterly reports. It was found that sophisticated models like GPT-4 outperformed less advanced models such as GPT-3, especially when using the ReAct framework, by providing more accurate and comprehensive responses. Conversely, when using simpler models, adding constraints to agent interactions and enhancing tool capabilities improved performance. The research highlighted that complex reasoning loops are most effective with advanced models, while simpler models benefit from tool-specific enhancements. The findings underscore the importance of choosing appropriate interaction frameworks based on the capabilities of the language models being employed.