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From model to agent: Equipping the Responses API with a computer environment

OpenAI Blog 11 March 2026, 11:00

OpenAI built an agent runtime that extends its Responses API with shell tools and hosted containers to enable agents to execute tasks with file access, tool integration, and persistent state. The system uses sandboxed environments that can run code securely while maintaining state across multiple interactions. This allows developers to create agents that perform multi-step operations without rebuilding context for each step.

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How OpenAI built an agent runtime using the Responses API, shell tool, and hosted containers to run secure, scalable agents with files, tools, and state.

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OpenAI AI Agents Agent Orchestration

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TLDRocket 21 July 2026

The open-weight model wars just got hotter and more complicated. Alibaba's decision to open-source Qwen 3.8 Max, a 2.4-trillion-parameter model, arrives as Kimi K3—Zhipu's 2.8T parameter system—has already claimed the top spot on Frontend Web App Arena with a 1326 Elo rating, outpacing even frontier models on certain benchmarks. Both moves signal that the frontier of capable AI is no longer exclusively held by closed API providers, a shift that's forcing the U.S. administration to consider layered policy restrictions on Chinese open models. Yet technical voices inside and outside government are pushing back, arguing that constraining open-weight systems paradoxically weakens security and competition rather than strengthening either. Meanwhile, the infrastructure problems keeping teams awake have matured: model routing, world modeling for agents, and long-horizon reliability are now first-class concerns, not afterthoughts. Frontier models have even begun solving mathematical problems at superhuman levels—like finding counterexamples to the 3D Jacobian conjecture—suggesting the bottleneck has shifted from raw capability to engineering deployment.

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From model to agent: Equipping the Responses API with a computer environment

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