In the rapidly evolving landscape of AI coding agents, the transport layer has emerged as a critical factor in determining performance and efficiency. This article delves into the significance of stateful continuation and the advantages of transport layers, particularly WebSocket mode, in enhancing the speed and efficiency of agentic coding workflows. By examining the 'Airplane Problem' and the 'Agentic Coding Loop', we explore how the traditional stateless HTTP approach can lead to inefficiencies, especially in multi-turn, tool-heavy loops. The introduction of WebSocket mode by OpenAI in February 2026 has revolutionized the way these workflows are handled, offering a more efficient alternative to HTTP. The author, Anirudh Mendiratta, presents a comprehensive analysis of the benefits of WebSocket mode, including a detailed benchmark study using GPT-5.4 and GPT-4o-mini models. The results demonstrate a 29% faster end-to-end execution, 82% less client-side data sent, and 11% lower TTFT with WebSocket mode compared to HTTP. The article also discusses the architectural implications of WebSocket mode, highlighting the importance of server-side state management and the trade-offs involved in stateful designs. Furthermore, it explores the 'Statefulness Spectrum' and the 'Parallel Execution' aspects, providing insights into when HTTP is still the right choice. The author concludes by emphasizing the need for a standard for stateful LLM continuation, and the potential for WebSocket mode to become a competitive advantage in the AI coding agent ecosystem.
