We’re proud to release this ahead of Ryan’s keynote at AIE Europe. Hit the bell, get notified when it is live! Attendees: come prepped for Ryan’s AMA with Vibhu after. Move over, context engineering. Now it’s time for Harness engineering and the age of the token billionaires. Ryan Lopopolo of OpenAI is leading that charge, recently publishing a lengthy essay on Harness Eng that has become the talk of the town: In it, Ryan peeled back the curtains on how the recently announced OpenAI Frontier team have become OpenAI’s top Codex users, running a >1m LOC codebase with 0 human written code and, crucially for the Dark Factory fans, no human REVIEWED code before merge. Ryan is admirably evangelical about this, calling it borderline “negligent” if you aren’t using >1B tokens a day (roughly $2-3k/day in token spend based on market rates and caching assumptions): Over the past five months, they ran an extreme experiment: building and shipping an internal beta product with zero manually written code. Through the experiment, they adopted a different model of engineering work: when the agent failed, instead of prompting it better or to “try harder,” the team would look at “what capability, context, or structure is missing?” The result was Symphony, “a ghost library” and reference Elixir implementation (by Alex Kotliarskyi) that sets up a massive system of Codex agents all extensively prompted with the specificity of a proper PRD spec, but without full implementation: The future starts taking shape as one where coding agents stop being copilots and start becoming real teammates anyone can use and Codex is doubling down on that mission with their Superbowl messaging of “you can just build things”. Across Codex, internal observability stacks, and the multi-agent orchestration system his team calls Symphony, Ryan has been pushing what happens when you optimize an entire codebase, workflow, and organization around agent legibility instead of human habit. We sat down with Ryan to dig into how OpenAI’s internal teams actually use Codex, why the real bottleneck in AI-native software development is now human attention rather than tokens, how fast build loops, observability, specs, and skills let agents operate autonomously, why software increasingly needs to be written for the model as much as for the engineer, and how Frontier points toward a future where agents can safely do economically valuable work across the enterprise. We discuss: * Ryan’s background from Snowflake, Brex, Stripe, and Citadel to OpenAI Frontier Product Exploration, where he works on new product development for deploying agents safely at enterprise scale * The origin of “harness engineering” and the constraint that kicked off the whole experiment: Ryan deliberately refused to write code himself so the agent had to do the job end to end * Building an internal product over five months with zero lines of human-written code, more than a million lines in the repo, and thousands of PRs across multiple Codex model generations * Why early Codex was painfully slow at first, and how the team learned to decompose tasks, build better primitives, and gradually turn the agent into a much faster engineer than any individual human * The obsession with fast build times: why one minute became the upper bound for the inner loop, and how the team repeatedly retooled the build system to keep agents productive * Why humans became the bottleneck, and how Ryan’s team shifted from reviewing code directly to building systems, observability, and context that let agents review, fix, and merge work autonomously * Skills, docs, tests, markdown trackers, and quality scores as ways of encoding engineering taste and non-functional requirements directly into context the agent can use * The shift from predefined scaffolds to reasoning-model-led workflows, where the harness becomes the box and the model chooses how to proceed * Symphony, OpenAI’s internal Elixir-based orchestration layer for spinning up, supervising, reworking, and coordinating large numbers of coding agents across tickets and repos * Why code is increasingly disposable, why worktrees and merge conflicts matter less when agents can resolve them, and what it really means to fully delegate the PR lifecycle * “Ghost libraries”, spec-driven software, and the idea that a coding agent can reproduce complex systems from a high-fidelity specification rather than shared source code * The broader future of Frontier: safely deploying observable, governable agents into enterprises, and building the collaboration, security, and control layers needed for real-world agentic work Ryan Lopopolo * X: https://x.com/_lopopolo * Linkedin: https://www.linkedin.com/in/ryanlopopolo/ * Website: https://hyperbo.la/contact/ Timestamps 00:00:00 Introduction: Harness Engineering and OpenAI Frontier00:02:20 Ryan’s background and the “no human-written code” experiment00:08:48 Humans as the bottleneck: systems thinking, observability, and agent workflows00:12:24 Skills, scaffolds, and encoding engineering taste into context00:17:17 What humans still do, what agents already own, and why software must be agent-legible00:24:27 Delegating the PR lifecycle: worktrees, merge conflicts, and non-functional requirements00:31:57 Spec-driven software, “ghost libraries,” and the path to Symphony00:35:20 Symphony: orchestrating large numbers of coding agents00:43:42 Skill distillation, self-improving workflows, and team-wide learning00:50:04 CLI design, policy layers, and building token-efficient tools for agents00:59:43 What current models still struggle with: zero-to-one products and gnarly refactors01:02:05 Frontier’s vision for enterprise AI deployment01:08:15 Culture, humor, and teaching agents how the company works01:12:29 Harness vs. training, Codex model progress, and “you can just do things”01:15:09 Bellevue, hiring, and OpenAI’s expansion beyond San Francisco Transcript Ryan Lopopolo: I do think that there is an interesting space to explore here with Codex, the harness, as part of building AI products, right? There’s a ton of momentum around getting the models to be good at coding. We’ve seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you’re trying to. Build a user journey that you’re trying to solve into code. It’s pretty natural to use the Codex Harness to solve that problem for you. It’s done all the wiring and lets you just communicate in prompts. To let the model cook, you have to step back, right? Like you need to take a systems thinking mindset to things and constantly be asking, where is the Asian making mistakes? Where am I spending my time? How can I not spend that time going forward? And then build confidence in the automation that I’m putting in place. So I have solved this part of the SDLC. swyx: [00:01:00] All right. [00:01:03] Meet Ryan swyx: We’re in the studio with Ryan from OpenAI. Welcome. Ryan Lopopolo: Hi, swyx: Thanks for visiting San Francisco and thanks for spending some time with us. Ryan Lopopolo: Yeah, thank you. I’m super excited to be here. swyx: You wrote a blockbuster article on harness engineering. It’s probably going to be the defining piece of this emerging discipline, huh? Ryan Lopopolo: Thank you. It is it’s been fun to feel like we’ve defined the discourse in some sense. swyx: Let’s contextualize a little bit, this first podcast you’ve ever done. Yes. And thank you for spending with us. What is, where is this coming from? What team are you in all that jazz? Ryan Lopopolo: Sure, sure. Ryan Lopopolo: I work on Frontier Product Exploration, new product development in the space of OpenAI Frontier, which is our enterprise platform for deploying agents safely at scale, with good governance in any business. And. The role of VMI team has been to figure out novel ways to deploy our models into package and products that we can sell as solutions to enterprises. swyx: And you have a background, I’ll just squeeze it in there. Snowflake, brick, [00:02:00] stripe, citadel. Ryan Lopopolo: Yes. Yes. Same. Any kind of customer swyx: entire life. Yes. The exact kind of customer that you want to, Vibhu: so I’ll say, I was actually, I didn’t expect the background when I looked at your Twitter, I’m seeing the opposite. Stuff like this. So you’ve got the mindset of like full send AI, coding stuff about slop, like buckling in your laptop on your Waymo’s. Yes. And then I look at your profile, I’m like, oh, you’re just like, you’re in the other end too. Oh, perfect. Makes perfect. Ryan Lopopolo: I it’s quite fun to be AI maximalist if you’re gonna live that persona. Open eye is the place to do it. And it’s swyx: token is what you say. Ryan Lopopolo: Yeah. Certainly helps that we have no rate limits internally. And I can go, like you said, full send at this stay. swyx: Yeah. Yeah. So the Frontier, and you’re a special team within O Frontier. Ryan Lopopolo: We had been given some space to cook, which has been super, super exciting. [00:02:47] Zero Code Experiment Ryan Lopopolo: And this is why I started with kind of a out there constraint to not write any of the code myself. I was figuring if we’re trying to make agents that can be deployed into end to enterprises, they should be [00:03:00] able to do all the things that I do. And having worked with these coding models, these coding harnesses over 6, 7, 8 months, I do feel like the models are there enough, the harnesses are there enough where they’re isomorphic to me in capability and the ability to do the job. So starting with this constraint of I can’t write the code meant that the only way I could do my job was to get the agent to do my job. Vibhu: And like a, just a bit of background before that. This is basically the article. So what you guys did is five months of working on an in
