
Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments. At NVIDIA, engineers are using Codex as their default tool for complex engineering work, and to run end-to-end machine learning experiments. Codex, built on GPT‑5.5 and running in production on NVIDIA GB200 and GB300 infrastructure, can handle much longer, more autonomous sessions — going beyond execution to surface issues and ideas that weren't part of the original prompt. The team has also built an internal podcast recording app, similar to Riverside, spun up in just hours using Codex.
At NVIDIA, engineers are using Codex as their default tool for complex engineering work, and to run end-to-end machine learning experiments. Codex, built on GPT‑5.5 and running in production on NVIDIA GB200 and GB300 infrastructure, can handle much longer, more autonomous sessions — going beyond execution to surface issues and ideas that weren't part of the original prompt.
NVIDIA’s coding agents team helps engineers across the company adopt and use AI tools effectively in real-world development workflows. Codex with GPT‑5.5 has become their go-to tool for complex engineering tasks.
“I’ve personally found Codex with GPT‑5.5 to be way more autonomous, with much less handholding,” explains Dennis Hannusch, a senior software engineer on the agents team. “I’m able to go for long sessions with multiple compactions and find that it still performs with top accuracy and manages to keep the work in context. And it’s great at tactically selecting the right tools as well as the right skills.”
The team uses Codex as a research agent, pointing the model at a large corpus of relevant papers in areas like reinforcement learning. “GPT‑5.5 seems to be much more creative compared to competitors,” Joshi says. “It helped me trace snippets of evidence throughout the entire chain, and suggested a knowledge graph of the ideas that really helped me visualize how concepts tied together.”
Codex is accelerating work at NVIDIA across engineering and research teams, taking ideas from concept to execution and testing in a single workflow.
Hannusch has already used Codex to evolve an internal platform from an MVP into a production-ready system, improving scalability and reliability along the way, something that had proven difficult with earlier models.
The team has also built an internal podcast recording app, similar to Riverside, spun up in just hours using Codex. “Given our privacy constraints, it would have taken us weeks to procure software,” Hannusch explains.
Using the Codex desktop app with computer interaction, the system was also able to test the video and audio recording functionality as it was built. “I didn’t have to do anything—it was built and tested completely autonomously,” he says. “Codex has completely changed the threshold for what’s worth building.”
Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments Reported by OpenAI on May 12, 2026.