Abstract: Deploying LLMs on edge devices is challenging due to stringent memory resources and compute constraints. In edge applications, existing deployment solutions for LLM agents disaggregate the fine-tuning process for domain-specific adaptation and the post-training model compression process. As a result, it requires extensive experimentation to find a readily available model compression technique that minimizes a fine-tuned model's performance loss while satisfying a target hardware's memory constraints. To address this problem, we propose TinyAgent, which optimizes the deployment workflow by using a quantization-aware model compression technique for specialized decision-making LLM agents under resource-constrained environments. Our approach takes into account both deployment-time hardware constraints and challenges in post-training quantization during fine-tuning. Experimental results demonstrate that our approach not only achieves 8$\times$ less memory usage to make LLM inference possible across a variety of edge devices, but also consistently speeds up LLM inference by up to $4.5\times$ without compromising accuracy.