Inside OpenAI’s push to build AI agents that can do anything for you

Shortly after joining OpenAI in 2022, researcher Hunter Lightman worked on a small team focused on improving AI’s ability to solve high school math competitions—a seemingly niche goal that would become central to the company’s broader mission. That team, known as MathGen, helped lay the foundation for OpenAI’s breakthrough in AI reasoning, a critical step toward building intelligent agents capable of performing complex tasks on a computer like a human. At the time, OpenAI’s models struggled with basic math. But by combining large language models with reinforcement learning and a technique called test-time computation—allowing models extra time and processing power to plan and verify their steps—OpenAI developed a new approach called chain-of-thought reasoning. This enabled models to work through problems step by step, detect errors, and backtrack, mimicking human-like thought processes. The result was a major leap forward. One of OpenAI’s models earned a gold medal at the International Math Olympiad, a feat once thought impossible for AI. This success validated the company’s strategy and paved the way for the development of o1, a reasoning model unveiled in late 2024. The model’s ability to reason, plan, and verify solutions marked a turning point in AI capabilities. The breakthrough was not accidental. It emerged from years of deliberate research, particularly in reinforcement learning—a training method that rewards correct behavior in simulated environments. While RL had been used before—most famously by Google DeepMind’s AlphaGo—it was OpenAI’s unique integration of RL with large language models and test-time computation that created a new frontier. Following the o1 success, OpenAI launched an “Agents” team led by researcher Daniel Selsam to build systems that could perform real-world tasks. The effort brought together top minds, including co-founder Ilya Sutskever, chief research officer Mark Chen, and chief scientist Jakub Pachocki. The company’s culture of bottom-up innovation allowed researchers to secure resources by demonstrating tangible progress, a key factor in driving the project forward. The impact of this work has been massive. The 21 researchers behind o1 are now among the most sought-after in Silicon Valley. Meta lured five of them to its new superintelligence unit, offering compensation packages exceeding $100 million. One, Shengjia Zhao, was named chief scientist of Meta Superintelligence Labs. OpenAI’s vision, as CEO Sam Altman has said, is for users to simply ask their computer to do something—and have it complete the task autonomously. While today’s AI agents still struggle with subjective or ambiguous tasks like online shopping or finding parking, researchers believe the core challenge is not in the concept, but in training data and methods. Lightman emphasizes that the goal isn’t to replicate human reasoning exactly, but to build systems that can perform hard tasks effectively. “If the model is doing hard things, then it’s doing whatever it needs to do to get there,” he says. Researchers like Nathan Lambert of AI2 compare AI reasoning to airplanes—inspired by birds but built on entirely different principles. The outcome matters more than the mechanism. OpenAI is now pushing further, developing new reinforcement learning techniques to train models on tasks that aren’t easily verifiable. Noam Brown, a key developer of the IMO-winning model, says OpenAI is using multi-agent systems where several AI agents collaborate, explore ideas, and select the best solution—similar to approaches now emerging at Google and xAI. These advances are expected to shape the next generation of AI, including GPT-5. OpenAI aims to make its agents not only more capable but also more intuitive—able to understand user intent, choose the right tools, and decide how long to think, without requiring explicit instructions. While OpenAI once led the AI race, it now faces intense competition from Google, Anthropic, xAI, and Meta. The race is no longer just about building smarter models, but about creating agents that can truly do anything for users. The question is no longer whether OpenAI can deliver on its vision—but whether it can do so first.