跟读练习: Introducing the Agents API - 通过视频学习英语口语
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25 句
如果句子过短或过长，请点击 Edit 进行调整。
1
Getting a long-running agent into production takes a lot of work, even with a capable model.
即便有能力出众的模型支撑，要让一个长期运行的智能体投入生产环境也需要大量工作。
2
You need to connect tools, track progress, manage context, and secure and maintain the infrastructure around it.
3
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.
5
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.
7
Suppose we want to build an agent that helps investigate incidents in our production stack.
8
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.
11
That includes connecting a sandbox, whether it's through through OpenAI, a third party provider, or using your own infrastructure.
12
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.
15
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.
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本课的词汇与口语要点
这节 C1 级别的口语课以视频“Introducing the Agents API”为素材。 视频中反复出现的词有：agent, infrastructure, Api, tool, context。 这段视频共有 25 个句子、392 个单词可供跟读。 讲话部分时长为 2:17。 说话人语速自然，每分钟约 172 个词，接近日常对话。 只有 81% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中最难的 8 个单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| orchestration 名词 | 配器 | |
| compaction 名词 | /kəmˈpækʃən/ | 壓實 /压实 |
| sandbox 名词 | /ˈsæn(d).bɑks/ | 砂箱 |
| workflow 名词 | /ˈwɝkfloʊ/ | 工作流 |
| delegate 名词 | /ˈdɛlɪɡət/ | 代表, 使節 /使节 |
| token 名词 | /ˈtoʊkən/ | 代幣 /代币, 籌碼 /筹码 |
| bundle 名词 | /ˈbʌn.dl̩/ | 捆, 包 |
| stack 名词 | /stæk/ | 堆 |
需要注意的发音
说话人用了 3 次缩略和弱读形式，例如 we're, we'd。请按听到的简短形式来说。
- “sh” 和 “zh” 音: compaction /kəmˈpækʃən/, shareable /ˈʃɛəɹ.ə.bəl/
- 长单词——注意重音位置: programmatic /ˌpɹəʊ.ɡɹəˈmæt.ɪk/, telemetry /tɪˈlɛm.ɪ.tɹi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
中文母语者容易读错的音：
- 词尾辅音 — 要读清楚，后面不要加元音: harness /ˈhɑɹ.nəs/, outage /ˈaʊ.tɪd͡ʒ/, programmatic /ˌpɹəʊ.ɡɹəˈmæt.ɪk/, sandbox /ˈsæn(d).bɑks/, delegate /ˈdɛlɪɡət/
- /r/ 和 /l/ 的区别: shareable /ˈʃɛəɹ.ə.bəl/, telemetry /tɪˈlɛm.ɪ.tɹi/
- /æ/ — 嘴张大，不要读成 /e/: compaction /kəmˈpækʃən/, programmatic /ˌpɹəʊ.ɡɹəˈmæt.ɪk/, sandbox /ˈsæn(d).bɑks/, stack /stæk/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 orchestration, compaction, sandbox 这类单词。
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。