쉐도잉 연습: Introducing the Agents API - 영상으로 영어 말하기 배우기

Getting a long-running agent into production takes a lot of work, even with a capable model.

You need to connect tools, track progress, manage context, and secure and maintain the infrastructure around it.

Today, we're launching the Agents API to handle that infrastructure for you.

The Agents API brings a hosted version of the Codex Harness to your applications.

OpenAI handles orchestration, sessions, and context management so that you can stay focused on building.

Let's look at an example.

Suppose we want to build an agent that helps investigate incidents in our production stack.

It would need access to observability data and recent code changes, along with our team's instructions for how to handle an outage.

We can connect all of our necessary tools through MCPs and give the agent our investigation runbook via a skill.

You control the agent's execution environment and the tools it has access to.

That includes connecting a sandbox, whether it's through through OpenAI, a third party provider, or using your own infrastructure.

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.

With programmatic tool calling, the agent can process those logs and filter the results in code,

meaning fewer tokens spent passing around raw data and more spent on the information the agent needs.

For the largest tasks, independent work can be delegated via multi-agent orchestration.

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.

But even in a single agent session, long-running context windows still work really well with compaction.

It gives the model a summary of all prior work completed so the agent can continue its investigation.

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,

and suggested next steps bundled into a single shareable file.

And there we have it.

With the agents API, we were able to drive an entire workflow without building or maintaining any of our own agent infrastructure.

Infrastructure that will continue to get better alongside new models and new capabilities.

We're excited to bring you an ever-improving harness behind one API.

Happy building.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

쉐도잉 연습: Introducing the Agents API - 영상으로 영어 말하기 배우기

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쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법