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OpenAI’s New Agent Stack: Computer Use, Decisions API, UltraFast, Dots—Ari Weinstein & Nikunj Handa

From giving AI agents their own cloud computers to making Computer Use faster than humans at real-world tasks, OpenAI is pushing agents much closer to actually operating software end-to-end. In this episode, recorded immediately after OpenAI DevDay, Ari Weinstein, who leads product and engineering for Computer Use, joins swyx and Vibhu to unpack Dots, GPT-6.1, the Agents API, App Shots, and the rapid improvements that have made Computer Use dramatically faster, cheaper, and more reliable. Ari explains why Computer Use is now “180 degrees different” from where it was months ago, how agents are learning to debug and recover from failures, why combining screenshots with accessibility data, the DOM, Playwright, and generated code changes the speed equation, and why the next frontier is making agents literally superhuman at using software. In the second half, Nikunj from OpenAI’s API team breaks down the new developer stack: async tool calling, mid-turn steering, WebSockets, UltraFast inference, the Decisions API, prompt caching, pre-warming, compaction, and the Agents API. He also shares the unusually fast story behind Decisions API, how OpenAI’s inference teams are using coding agents to optimize their own systems, and the bigger question of whether OpenAI is effectively building an “AI cloud” with higher-level primitives for agents, memory, compute, and state. We discuss: • Why OpenAI thinks Computer Use has changed dramatically in just the last few months • Dots and what changes when every agent gets its own Linux computer • Why Computer Use can now complete some tasks faster than the average human • The path from human-level to “literally superhuman” software operation • Why modern Computer Use agents are much better at debugging and recovering from failure • How screenshots, accessibility trees, the DOM, Playwright, and generated JavaScript work together • App Shots and why they give models much richer context than ordinary screenshots • Why Computer Use can close the loop between writing software and testing it • Trust, permissions, and safety when agents can make payments or operate third-party websites • Async function calling and why models no longer need to stop reasoning while tools run • Mid-turn steering, WebSockets, and the architecture behind more responsive agents • UltraFast inference and how OpenAI is pushing frontier models toward much lower latency • The rapid internal story behind Decisions API • Why Decisions API is more than just structured outputs at low latency • GPT Live, fast tool calling, and real-time computer control • How OpenAI is using Decisions API internally for support classification and other workflows • Longer prompt caching, cache pre-warming, and building cache-aware applications • Server-side compaction vs manual compaction for long-running agent threads • What should live inside an Agents API versus inside a developer’s own harness • OpenAI as an “AI cloud” and the search for higher-level primitives beyond raw model APIs Sponsorship and business inquiries: business@latent.space — Ari Weinstein • Head of Product & Engineering, Computer Use at OpenAI Nikunj Handa • Head of Product, API at OpenAI Timestamps 00:00:00 Hook 00:01:12 OpenAI DevDay: Dots, GPT-6.1, Agents API, and Decisions API 00:04:04 Dots and Personal Cloud Computers 00:06:11 Why Computer Use Is “180 Degrees Different” 00:07:16 From Sky to Self-Debugging Computer Use Agents 00:10:36 How Computer Use Sees and Operates Software 00:13:21 From Faster Than Humans to Superhuman Computer Use 00:17:15 Agents API: Trust, Permissions, and Safety 00:18:43 Computer Use for Coding, Testing, and QA 00:20:26 GPT-6 APIs, Async Tool Calling, and UltraFast Inference 00:24:33 The Rapid Story Behind Decisions API 00:26:44 What Decisions API Is and How It Works 00:31:36 What OpenAI Is Building With the New APIs 00:33:35 Prompt Caching, Pre-Warming, and API Performance 00:36:32 Context Compaction for Long-Running Agents 00:38:25 Memory, Higher-Level APIs, and the AI Cloud

Ari WeinsteinguestswyxhostVibhuhostNikunj Handaguest

Sep 30, 202640mWatch on YouTube ↗

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EPISODE INFO

Released: September 30, 2026
Duration: 40m
Channel: Latent Space
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

From giving AI agents their own cloud computers to making Computer Use faster than humans at real-world tasks, OpenAI is pushing agents much closer to actually operating software end-to-end. In this episode, recorded immediately after OpenAI DevDay, Ari Weinstein, who leads product and engineering for Computer Use, joins swyx and Vibhu to unpack Dots, GPT-6.1, the Agents API, App Shots, and the rapid improvements that have made Computer Use dramatically faster, cheaper, and more reliable. Ari explains why Computer Use is now “180 degrees different” from where it was months ago, how agents are learning to debug and recover from failures, why combining screenshots with accessibility data, the DOM, Playwright, and generated code changes the speed equation, and why the next frontier is making agents literally superhuman at using software. In the second half, Nikunj from OpenAI’s API team breaks down the new developer stack: async tool calling, mid-turn steering, WebSockets, UltraFast inference, the Decisions API, prompt caching, pre-warming, compaction, and the Agents API. He also shares the unusually fast story behind Decisions API, how OpenAI’s inference teams are using coding agents to optimize their own systems, and the bigger question of whether OpenAI is effectively building an “AI cloud” with higher-level primitives for agents, memory, compute, and state. We discuss:
Why OpenAI thinks Computer Use has changed dramatically in just the last few months
Dots and what changes when every agent gets its own Linux computer
Why Computer Use can now complete some tasks faster than the average human
The path from human-level to “literally superhuman” software operation
Why modern Computer Use agents are much better at debugging and recovering from failure
How screenshots, accessibility trees, the DOM, Playwright, and generated JavaScript work together
App Shots and why they give models much richer context than ordinary screenshots
Why Computer Use can close the loop between writing software and testing it
Trust, permissions, and safety when agents can make payments or operate third-party websites
Async function calling and why models no longer need to stop reasoning while tools run
Mid-turn steering, WebSockets, and the architecture behind more responsive agents
UltraFast inference and how OpenAI is pushing frontier models toward much lower latency
The rapid internal story behind Decisions API
Why Decisions API is more than just structured outputs at low latency
GPT Live, fast tool calling, and real-time computer control
How OpenAI is using Decisions API internally for support classification and other workflows
Longer prompt caching, cache pre-warming, and building cache-aware applications
Server-side compaction vs manual compaction for long-running agent threads
What should live inside an Agents API versus inside a developer’s own harness
OpenAI as an “AI cloud” and the search for higher-level primitives beyond raw model APIs
Sponsorship and business inquiries: business@latent.space — Ari Weinstein
• Head of Product & Engineering, Computer Use at OpenAI Nikunj Handa
• Head of Product, API at OpenAI Timestamps 00:00:00 Hook 00:01:12 OpenAI DevDay: Dots, GPT-6.1, Agents API, and Decisions API 00:04:04 Dots and Personal Cloud Computers 00:06:11 Why Computer Use Is “180 Degrees Different” 00:07:16 From Sky to Self-Debugging Computer Use Agents 00:10:36 How Computer Use Sees and Operates Software 00:13:21 From Faster Than Humans to Superhuman Computer Use 00:17:15 Agents API: Trust, Permissions, and Safety 00:18:43 Computer Use for Coding, Testing, and QA 00:20:26 GPT-6 APIs, Async Tool Calling, and UltraFast Inference 00:24:33 The Rapid Story Behind Decisions API 00:26:44 What Decisions API Is and How It Works 00:31:36 What OpenAI Is Building With the New APIs 00:33:35 Prompt Caching, Pre-Warming, and API Performance 00:36:32 Context Compaction for Long-Running Agents 00:38:25 Memory, Higher-Level APIs, and the AI Cloud

SPEAKERS

Ari Weinstein
guest
OpenAI product/engineering lead for Computer Use agents; previously worked at Apple and co-founded Sky.
swyx
host
Co-host of Latent Space and organizer of the AI Engineer community/events.
Vibhu
host
Co-host of Latent Space who drives the interview and transitions between guests.
Nikunj Handa
guest
OpenAI API product lead (around 3 years), formerly at Stripe, discussing Agents/Responses and Decisions APIs.

EPISODE SUMMARY

In this episode of Latent Space, featuring Ari Weinstein and swyx, OpenAI’s New Agent Stack: Computer Use, Decisions API, UltraFast, Dots—Ari Weinstein & Nikunj Handa explores openAI DevDay deep dive: faster Computer Use agents and Decisions API OpenAI DevDay announcements discussed include Dots with personal cloud computers, Computer Use inside the Agents API, App Shots, a new GPT-6.1 Sol model positioned as cost- and speed-advantaged for Computer Use, and the new Decisions API.

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