When I tried GPT-Live-1 today, I felt deflated as I recognized behaviors we’d spent weeks trying to make reliable. For the past three months, I’ve been focusing on researching and building duplex voice agents, where I wrote thousands of lines across our harness and the experiments around it. Customers use parts of that work every day, the conversions are increasing drastically as we make our voice agents more reliable and expressive. This duplex voice harness/model (interaction runtime + kernel) problem has occupied most of my attention but tbh when I first started working with voice agents, I didn't know how challenging and complex they were. Duplex conversational timing, orchestration, acoustic turn-taking, micro-interruptions, prosody, and generalized noise etc are really hard problems. There are so many 'beautifully crafted"'voice demos but seeing them in production is almost impossible. A cough would interrupt the voice agent, it leaves the caller waiting for it to speak again. During a tool call, someone would change their decision before the earlier result returned. There are also many other telephony factors so it is not just an AI or research problem. Following those cases through the system led us to separate the part that speaks from the work running in the background. We’ve developed a kernel that controls what the agent can do and checks what happened before it reports an outcome to the caller because the duplex models simply weren’t good enough yet. During this period, reading NVIDIA’s PersonaPlex and Nemotron VoiceChat work helped me understand where the models were heading; Thinking Machines’ Interaction Models approach to keeping an interaction going during longer tasks connected with questions we were already working through. My first thought was how much of what I’ve been working on will we no longer need? This is another great example of how harnesses are compressing into models. That's why always build your harness and repo that you can destroy every 6 months and build it from scratch again. It should be very fluid and modular, so you can easily bring the frontier capabilities. That's why building a duplex harness that happens to use ASR, STT, LLM, TTS models is not a good solution since duplex is commoditized today. Build a harness where the duplex model itself is a replaceable component. This is valid for any software field. After spending months with these problems, I appreciate the work behind GPT-Live more deeply. Through client delegation, anyone can connect GPT-Live to existing harnesses, where the models you choose work with your tools. Thank you to the OpenAI team. This is a big win for anyone building solutions to benefit humanity.