シャドーイング練習: Introducing the Agents API - 動画で英語スピーキングを学ぶ
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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.
このレッスンについて
「Introducing the Agents API」を使って、シャドーイングで英語を練習しましょう。
毎日15〜30分の練習で、IELTSスピーキングへの自信と実践的な英会話力が身につきます。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。