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OpenAI Introduces ChatGPT Agent: From Research to Real-World Automation
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OpenAI最新推出的ChatGPT Agent,将ChatGPT从单纯的对话助手升级为能够自主执行复杂多步任务的AI代理。它整合了网页浏览、代码执行等能力,在一个虚拟计算机环境中运行。该Agent融合了之前Operator和Deep Research工具的优势,实现了动态交互与深度分析的统一。其内部架构包含视觉浏览器、文本浏览器、代码终端和API连接器,能够根据任务需求灵活切换和执行。ChatGPT Agent在多项基准测试中展现出显著性能提升,例如在Humanity’s Last Exam中达到41.6%的Pass@1率。为应对自主化带来的风险,OpenAI实施了多重安全措施,包括行动确认、观察模式、防注入和隐私保护。该功能已向Pro、Plus和Team用户逐步开放,标志着AI助手正从被动回应走向主动的、可控的自动化工作流程。

✨ **ChatGPT Agent的核心能力与演进**:ChatGPT Agent标志着AI助手从被动对话向主动执行的重大转变。它将之前的Operator(有限网页交互)和Deep Research(自主浏览与报告合成)的优势相结合,在一个统一的虚拟计算机环境中,能够自主执行包括网页浏览、代码执行在内的复杂多步任务,实现了真正的AI代理能力。

💻 **强大的内部架构与工作流程**:Agent的核心是一个集成的虚拟计算机环境,内含用于用户界面的视觉浏览器、用于结构化推理的文本浏览器、用于代码执行的Shell/Terminal,以及连接Gmail、GitHub等服务的API连接器。这种设计使得Agent能够根据任务需求,在不同工具间无缝切换,并能跨工具保持状态,实现高度的灵活性和可控性。

🚀 **显著的性能提升与应用场景**:在多项基准测试中,ChatGPT Agent展现出优于以往模型的性能,例如在Humanity’s Last Exam中Pass@1率达到41.6%,在SpreadsheetBench中得分45.5%,接近人类水平。它能够胜任如日历简报、杂货订购、竞争对手分析、财务建模等多种实际工作,将AI的能力从信息处理延伸到实际操作。

🛡️ **安全与风险缓解机制**:为应对AI自主化带来的潜在风险,OpenAI部署了多层安全措施,包括在执行关键操作前需要用户明确确认、敏感任务的“观察模式”、强大的提示注入防御机制,以及会话期间的隐私保护。此外,针对生物威胁等高风险领域,还实施了增强的威胁建模和监控。

📈 **AI工作流的未来展望**:ChatGPT Agent的推出预示着AI助手将成为“主动的数字工作者”,通过语言推理、工具编排和上下文保持的结合,实现更自主、可靠和面向行动的应用。它不仅为用户提供了更强大的AI助手,也为开发者和数据科学家提供了一个可编程、可观察的平台,有望重塑研究、业务自动化和个人生产力等领域的工作流程。

On July 17, 2025, OpenAI launched ChatGPT Agent, transforming ChatGPT from a conversational assistant into a unified AI agent capable of autonomously executing complex, multi‑step tasks—from web browsing to code execution—on a virtual computer environment.

Bridging Previous Capabilities

ChatGPT Agent builds on two earlier tools:

Individually, both had limitations: Operator could interface but couldn’t perform in‑depth analysis; Deep Research could analyze but not interact dynamically with sites. ChatGPT Agent merges both strengths, unifying browsing, tool use, and reasoning inside a single agentic architecture.

Internal Architecture and Workflow

At the core is a virtual computer environment combining:

    A visual browser for human‑facing sites,A text browser optimized for structured reasoning,A shell/terminal for executing code,Integrated API connectors for services like Gmail or GitHub.

The agent continuously adapts—deciding whether to click buttons, run scripts, or parse content—while maintaining state across tools. All actions occur within controlled agent context, ensuring traceability and flexibility.

Example Tasks: From Planning to Execution

ChatGPT Agent can tackle tasks such as:

These workflows involve multi‑modal tool usage: logging into sites, running scripts in the terminal, then packaging results into editable docs—all with your oversight.

Performance: Benchmarks and Human Comparisons

OpenAI reports significant gains across multiple benchmarks:

These evaluations demonstrate a marked improvement in both autonomy and task sophistication.

Safety and Risk Mitigation

Agentic autonomy introduces new risks. OpenAI has implemented several safeguards:

These layers aim to reduce misuse—from data leaks to task hijacking.

How to Get Started

Available now to ChatGPT Pro, Plus, and Team users:

You can switch into “Agent Mode” via the tools menu in any conversation and describe your desired workflow. Progress is narrated in real‑time, and you can pause, take over, or stop at any moment.

Significance for AI‑augmented workflows

ChatGPT Agent represents a leap from passive query‑response systems to proactive digital workers. By combining:

…OpenAI is enabling more autonomous, reliable, and action‑oriented use cases. While controls are essential to guard against misuse, this release broadens the scope of what AI assistants can actually do, not just say.

For developers and data scientists, ChatGPT Agent becomes a platform: a programmable, observable agent capable of scraping, parsing, synthesizing, and exporting on demand. It opens opportunities for next‑gen workflows in research, business automation, and personal productivity.

Conclusion

ChatGPT Agent isn’t just a conversational enhancement—it’s a strategic pivot toward generalized, autonomous AI workflows. Its debut marks the transition of LLMs from passive advisers to active agents, performing research, creation, and real‑world action in a unified, controllable environment. Expect this to mature into a foundational capability across AI‑augmented domains.


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