Agents are now producing bug fixes faster than people can review them. In the time it took humans to verify 70,000 security patches, @OpenAI's agents verified 500,000 across major web browsers. At that pace, the human signing off at the end likely isn't reading most of it. Every lab keeps that human anyway. Probably not to catch the errors, but because someone has to be accountable when an agent is wrong. 👇 ------------------- That 7-to-1 gap isn't a one-off. @Google moved production models from TensorFlow to JAX at 6 to 8 times engineer speed, and on @YouTube's models, work that would have taken engineer-months finished in weeks. The agent does the volume, and a human still signs the bottom line. Nobody read 500,000 patches. The volume an agent clears in a day is more than a human gets through in a month, so the person approving Google's migration or OpenAI's security work is mostly taking it on trust. Anyone running a pipeline like this can see the same math. So why keep the human? Most likely for accountability. When something ships and breaks, someone has to own it, and "the agent approved it" isn't a sentence a company can offer a regulator or a court. The human is the name on the decision when it goes wrong, which may be the real job long after the reading stopped being possible. So, when do we go fully autonomous? It sounds like a question about agent capabilities. But on this evidence they're already good enough to outproduce the humans watching them. What's holding autonomy back is that removing the human removes the accountability, and nobody has built the replacement. A human's sign-off carries weight because a person can be held responsible. An agent's approval is just an output, with usually no record of what was checked, against what standard, that anyone can stand behind later. So companies keep a human in the loop as cover, reviewing a fraction of the work, because the alternative is an agent whose approval means nothing when it counts. Most of the field is racing to make agents more capable. Far fewer are building the thing that lets an agent's work be trusted without a human babysitting it. That's part of the infra we build at Sahara AI: verifiable execution and an unforgeable record of what an agent did and what it checked, so its sign-off can be held to account instead of taken on faith. Follow @SaharaAI to keep up with agentic AI, the infrastructure it demands, and where the real building is happening. About Sahara AI: Sahara AI is the agentic AI company dedicated to making AI more accessible and equitable. We build the core protocols, infrastructure, and applications that let personal agents anticipate and execute on your behalf. For this to work, infrastructure has to be trustworthy: verifiable execution, enforceable usage policies, and automatic value distribution across every tool, model, and service an agent touches. Sahara is building a growing suite of agent-powered applications on top of this foundation, including @HeySorinAI, your personal agent for global digital markets. Our solutions currently power AI agents and high-quality data for consumers, Fortune 500 enterprises, and leading research labs, including @Microsoft, @Amazon, @MIT, Motherson, and @Snap.(Sahara AI 🔆)