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Introducing the Agents API

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2:18
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inglés
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5.92s
Rostro en pantalla
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Transcripción de Introducing the Agents API

Transcrito por un modelo de voz a partir del audio del video, no redactado por la marca. Haz clic en cualquier línea para saltar a ese momento.

  1. 0:00Getting a long-running agent into production takes a lot of work, even with a capable model.
  2. 0:05You need to connect tools, track progress, manage context, and secure and maintain the infrastructure around it.
  3. 0:11Today, we're launching the Agents API to handle that infrastructure for you.
  4. 0:15The Agents API brings a hosted version of the Codex Harness to your applications.
  5. 0:20OpenAI handles orchestration, sessions, and context management so that you can stay focused on building.
  6. 0:26Let's look at an example.
  7. 0:28Suppose we want to build an agent that helps investigate incidents in our production stack.
  8. 0:32It would need access to observability data and recent code changes, along with our team's
  9. 0:37instructions for how to handle an outage. We can connect all of our necessary tools through MCPs
  10. 0:43and give the agent our investigation runbook via a skill. You control the agent's execution
  11. 0:49environment and the tools it has access to. That includes connecting a sandbox, whether it's through
  12. 0:54through OpenAI, a third-party provider, or using your own infrastructure.
  13. 0:58In a situation like this, we'd also be working through a ton of logs, likely more than we
  14. 1:04could fit in the model's context window.
  15. 1:06With programmatic tool calling, the agent can process those logs and filter the results
  16. 1:11in code, meaning fewer tokens spent passing around raw data and more spent on the information
  17. 1:16the agent needs.
  18. 1:18For the largest tasks, independent work can be delegated via multi-agent orchestration.
  19. 1:23In 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.
  20. 1:32But even in a single-agent session, long-running context windows still work really well with compaction.
  21. 1:38It gives the model a summary of all prior work completed so the agent can continue its investigation.
  22. 1:44Once the work is complete, the findings should be served as a report that the on-call team can review,
  23. 1:49a likely root cause, the supporting evidence, and suggested next steps
  24. 1:54bundled into a single shareable file.
  25. 1:56And there we have it.
  26. 1:57With the Agent's API, we were able to drive an entire workflow
  27. 2:01without building or maintaining any of our own agent infrastructure.
  28. 2:05Infrastructure that will continue to get better alongside new models and new capabilities.
  29. 2:10We're excited to bring you an ever-improving harness behind one API.
  30. 2:15Happy building.
30 líneas · 384 palabras

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