Skip to content

We use analytics and advertising tools by default. You can update this anytime.<br>Privacy PreferencesDo Not Sell or Share

Privacy Preferences<br>Manage optional tracking categories. Necessary cookies stay on so the site can function.<br>AnalyticsAdvertising and sharingCancelSave Preferences

Inside OpenAI’s Race to Reinvent Software Development for the Agent Era

E logo

This is the promise of virtually every billboard in San Francisco: The agentic future has arrived, automating away the need for human engineers.

The reality, as is typically the case, is more complicated than the billboards would have you believe.

Sometime in the middle of the night, a coding agent running on a researcher’s laptop posted a Slack message about a failed data export.

The researcher was asleep. So was James Katz, the infrastructure engineer on call that evening.

For its part, the agent was unfazed by the late hour. It had access to the tools and internal systems it needed to debug the problem.

And so while it waited for a response, it got to work, diligently narrating its efforts to diagnose the incident, investigate potential fixes, and restrategize whenever it hit a roadblock.

By morning, it had handed the researcher the data they needed, without Katz or anyone else on his team having to step in.

This is the promise of virtually every billboard in San Francisco: The agentic future has arrived, automating away the need for human engineers.

The reality, as is typically the case, is more complicated than the billboards would have you believe.

Before the Deluge

Inside OpenAI’s race to redesign software development for a world where agents write all of the code

By Laura Entis · Photographs by Sarah Deragon and Sarah Jay Halliday

Amiddle-aged woman speaks into an intercom outside a sleek, nondescript office building in San Francisco’s Mission Bay district.

“I’m here for lunch.”

“For lunch, ma’am?” A tinny voice crackles through the speaker. A tall, bearded security guard intercedes at the door before the conversation can go any further. “This is not a restaurant.”

Indeed, it is not. At 1515 3rd Street, better known as OpenAI’s headquarters, you need an invitation just to enter the building. Once inside, you’re greeted in a lobby appointed like a slightly corporate five-star hotel, complete with accent books and lemon-wheeled water coolers.

OpenAI may not be a restaurant, but a few floors up, it is lunchtime. Employees all working toward the company’s stated mission of achieving Artificial General Intelligence (AGI) line up for harvest bowls, tacos, stir fry, and hot slices fresh out of what I’m told is San Francisco’s largest live-flame pizza oven.

As OpenAI competes with rival Anthropic to release new frontier models at an increasingly rapid clip, its engineers can ship code 10, even 100 times faster than before AI. Codex, the company’s coding agent and desktop app, extends that capacity beyond engineering; product managers, data scientists, researchers, and go-to-market employees use it to write, modify, debug, and submit code that runs on OpenAI’s internal systems. Many in the cafeteria are glued to their phones, perhaps texting, scrolling social media or, more likely, orchestrating agents through OpenAI’s mobile app.

It’s an exhilarating time for anyone building new products or tools. For the engineers responsible for maintaining the internal systems that make all this building possible, however, the feeling is closer to staying afloat.

We use ‘deluge’ a lot,” says Emma Tang , who heads the applied infrastructure data platform team. Tang’s group maintains the infrastructure that moves, analyzes, and secures the data flowing through the plumbing of every OpenAI product, including ChatGPT and Codex. The team’s volume of pull requests (PRs), or proposed changes to a shared codebase, has jumped five- to tenfold in the past year.

A meaningful share of that code arrives from people in non-technical parts of the organization. “We have users generating Flink jobs”—programs built with Apache Flink, a system that analyzes continuous streams of product data—“who don’t even know Flink,” Tang says. As the pace of change has increased, so have internal reliability problems, from delayed data processing to dashboards that fail to load. And when something breaks, the author often can’t help because they don’t fully understand what they’ve shipped.

Until recently, if someone in operations or on the go-to-market team wanted a custom internal app, they’d need to find an engineer to make it for them. Now, anyone at the company can “show up on Monday with a working prototype they built over the weekend,” Tang says. Her engineers act increasingly as educators and mediators, helping colleagues turn their AI-generated tools into reliable software. A vibe-coded app, for example, might unnecessarily refresh every few seconds, while the analytics system it pulls from updates only once a day. Making the data truly live...