  The emergence of Large Language Models (LLMs) have fundamentally altered the
way we interact with digital systems and have led to the pursuit of LLM powered
AI agents to assist in daily workflows. LLMs, whilst powerful and capable of
demonstrating some emergent properties, are not logical reasoners and often
struggle to perform well at all sub-tasks carried out by an AI agent to plan
and execute a workflow. While existing studies tackle this lack of proficiency
by generalised pretraining at a huge scale or by specialised fine-tuning for
tool use, we assess if a system comprising of a coalition of pretrained LLMs,
each exhibiting specialised performance at individual sub-tasks, can match the
performance of single model agents. The coalition of models approach showcases
its potential for building robustness and reducing the operational costs of
these AI agents by leveraging traits exhibited by specific models. Our findings
demonstrate that fine-tuning can be mitigated by considering a coalition of
pretrained models and believe that this approach can be applied to other
non-agentic systems which utilise LLMs.
