How agents are transforming work
AI agents are transforming how we work by performing complex, long-horizon tasks, exceeding the capabilities of traditional chatbots. This shift is particularly evident at OpenAI, where internal adoption of their agentic tool, Codex, has grown exponentially across all departments, including non-technical ones.
Agentic AI is revolutionizing knowledge work by shifting from short, single interactions to delegated, long-horizon tasks. Unlike chatbots, agents can operate autonomously for extended periods, orchestrating tool calls, interacting with environments, and iterating towards solutions. This capability makes them the most powerful AI tool for various professional applications.
OpenAI's internal experience with Codex highlights this transformation. Initially, ChatGPT was the dominant AI tool within OpenAI. However, Codex adoption grew significantly across all departments, including Legal and Recruiting, becoming their primary AI tool by mid-2025. This rapid shift underscores the expanded capabilities and accessibility of agentic tools.
Codex’s increased capabilities have led to users tackling more difficult, longer-duration tasks. A significant portion of Codex requests now involve tasks estimated to take a person more than an hour to complete. Heavy users at OpenAI are running many hours of agent work daily, often in parallel, indicating a move towards orchestrating multiple agent tasks.
While engineers initially adopted Codex, its use quickly expanded to non-developers. By early June 2026, non-developer individual users multiplied 137 times, and non-developer organizational users increased 189-fold. This demonstrates that agentic tools are empowering a broader range of professionals to perform tasks that once required specialized technical expertise.
Related articles
OpenAI’s Jalapeño chip is Big Tech’s spiciest move away from Nvidia
OpenAI is challenging Nvidia's dominance in the AI chip market with its new custom inference chip, Jalapeño. This move positions OpenAI alongside other tech giants like Google and Apple, who are developing their own silicon to reduce reliance on single suppliers and gain more control over hardware performance.
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
AlgoEvolve introduces a novel approach to algorithmic trading by leveraging large language models (LLMs) for meta-evolution. This method allows for the creation of more adaptive and efficient trading programs. The research explores the potential of LLMs to revolutionize financial trading strategies.
Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
This research paper explores an LLM-powered pipeline for the comparative governance of Decentralized Autonomous Organizations (DAOs) and corporate AI protocols. It introduces an agentic analysis approach to understand and compare the regulatory frameworks of these distinct AI infrastructures.