Flux represents a novel approach to conversational speech recognition by integrating conversational state modeling with traditional speech-to-text systems, resulting in a more seamless and natural dialogue experience for voice agents. This method aims to address the limitations of current state machine models, which often struggle with robustness and consistency, leading to suboptimal interactions. By combining conversational flow and transcription into a single end-to-end system, Flux reduces latency and enhances accuracy, offering a more consistent experience than traditional models. It also allows for greater configurability, enabling developers to fine-tune the balance between precision, recall, and latency according to their needs. Unlike systems that handle speech-to-text and conversational modeling separately, Flux employs bidirectional information flow, which improves both transcription quality and conversational understanding. This integrated approach ensures that voice agents can deliver timely and contextually relevant responses, even under varying conditions, ultimately advancing the field of conversational AI by prioritizing both linguistic and acoustic cues within a unified model.