跟读练习: Introducing the Agents API - 通过视频学习英语口语
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Getting a long-running agent into production takes a lot of work, even with a capable model.
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You need to connect tools, track progress, manage context, and secure and maintain the infrastructure around it.
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Today, we're launching the Agents API to handle that infrastructure for you.
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The Agents API brings a hosted version of the Codex Harness to your applications.
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OpenAI handles orchestration, sessions, and context management so that you can stay focused on building.
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Let's look at an example.
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Suppose we want to build an agent that helps investigate incidents in our production stack.
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It would need access to observability data and recent code changes, along with our team's instructions for how to handle an outage.
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We can connect all of our necessary tools through MCPs and give the agent our investigation runbook via a skill.
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You control the agent's execution environment and the tools it has access to.
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That includes connecting a sandbox, whether it's through through OpenAI, a third party provider, or using your own infrastructure.
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In a situation like this, we'd also be working through a ton of logs, likely more than we could fit in the model's context window.
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With programmatic tool calling, the agent can process those logs and filter the results in code,
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meaning fewer tokens spent passing around raw data and more spent on the information the agent needs.
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For the largest tasks, independent work can be delegated via multi-agent orchestration.
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In this case, we might want one sub-agent to examine recent changes, another to check telemetry, and then have the lead agent unify their findings.
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But even in a single agent session, long-running context windows still work really well with compaction.
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It gives the model a summary of all prior work completed so the agent can continue its investigation.
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Once the work is complete, the findings should be served as a report that the on-call team can review, a likely root cause, the supporting evidence,
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and suggested next steps bundled into a single shareable file.
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And there we have it.
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With the agents API, we were able to drive an entire workflow without building or maintaining any of our own agent infrastructure.
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Infrastructure that will continue to get better alongside new models and new capabilities.
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We're excited to bring you an ever-improving harness behind one API.
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Happy building.
关于本课
您正在使用跟读技巧通过视频"Introducing the Agents API"练习英语口语和发音。
每天练习15到30分钟，将显著提高您的英语流利度和发音准确度。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。