Engineering fast, performant AI agents through fine-tuning | Decagon

What this post added

This post details Decagon's strategy for engineering fast and performant AI agents by fine-tuning specialized models. It explains the limitations of off-the-shelf models for customer-facing tasks and introduces the architecture's reliance on a network of specialized models. The post elaborates on two key fine-tuning techniques: supervised fine-tuning (SFT) for training on curated examples and reinforcement learning (RL) for optimizing based on quantifiable rewards. It highlights the benefits of this approach, including improved accuracy, reduced latency, and modularity for continuous evaluation and refinement, enabling model-agnostic experimentation and rapid performance evolution.