वीडियो पर वापस

Warp

Your agent skills should improve themselves

प्लैटफ़ॉर्म
YouTube
अवधि
3:24
बोली जाने वाली भाषा
अमेरिकी अंग्रेज़ी
व्यूज़
800
स्क्रीन पर चेहरा
89%
गहरे फ़्रेम
0%
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Your agent skills should improve themselves की ट्रांसक्रिप्ट

फ़िल्म के अपने ऑडियो से एक स्पीच मॉडल द्वारा लिखा गया, ब्रांड द्वारा नहीं। उस पल पर जाने के लिए किसी भी पंक्ति पर क्लिक करें।

  1. 0:00Let's talk about self-improvement loops for agents. The concept is pretty simple. We have a number of
  2. 0:05conversations with our agents every day across our team, and there are a number of failure points in
  3. 0:10those conversations where agents may hold the skills wrong or write inefficient code that we
  4. 0:15have to go revise, and it would be nice to have a system that's evaluating those conversations as
  5. 0:20they happen and suggesting improvements to our skills so that the agent performs better next time.
  6. 0:25To address this, we've created a three-step system that works off of all of your conversation data.
  7. 0:31It starts from aggregating all of your raw conversations, turning those into scored conversations,
  8. 0:37so defining rubrics of what success and failure look like, and then having an agent review all of those scores to suggest improvements.
  9. 0:45Here I'll show you what that looks like implemented as part of a larger software factory.
  10. 0:50This dashboard is part of Warp Factories, which you can get early access to right now.
  11. 0:55And you can see here we've set up a number of scorers that we run across all the conversations
  12. 1:00across our team.
  13. 1:01Here we've created a code quality agent that reviews all the conversations we have working
  14. 1:05on our main repositories, and it evaluates whether it has passed or failed our code quality
  15. 1:11test.
  16. 1:12And we can see the configuration for that here, where whenever an agent is implementing
  17. 1:17code on our repositories, we've given it some instructions on how to evaluate the performance.
  18. 1:22This code quality agent is going to assess the design and correctness of the code that
  19. 1:26agents write, and it's also going to look into the diffs and documentation to suggest
  20. 1:32improvements.
  21. 1:32And here we've set the model that's going to judge all these conversations, along with
  22. 1:36classifications, which are basically a rubric from zero to one where you can define how
  23. 1:42the agent should judge that conversation.
  24. 1:44For code quality, it's fairly simple.
  25. 1:46It's going to be approved if the code is correct and consistent with our conventions.
  26. 1:51If there's insufficient evidence, it should bail out without explicitly failing, and it
  27. 1:56should block that PR if it finds evidence that it wasn't up to our quality standards.
  28. 2:01This agent will run on an automation every time an agent tries to open a PR, and it will
  29. 2:06assess the pass or failure over time.
  30. 2:08Here as you can see, we've been fine-tuning the success and failure criteria to get the
  31. 2:12best quality output.
  32. 2:14And then once we have agents grading these conversations, we can have another agent review
  33. 2:18all of the runs that failed our quality test and suggest improvements to our skills so they don't
  34. 2:24fail next time. And we can see samples of all of those improvement runs right here in our
  35. 2:28self-improvement dashboard. This is also featured as baked into warp factories. We can see here
  36. 2:34that a self-improvement agent reviewed all of those scored conversations and made some PRs to
  37. 2:40actually improve our skills. In this example, our code review agent wasn't fetching all of the
  38. 2:44necessary PR feedback before writing a review.
  39. 2:48And down here, we can see direct references
  40. 2:50to the failing scores that prompted this PR.
  41. 2:53Here, it was prompted by our efficiency and task compliance
  42. 2:57scores, which are other rubrics that we have set up,
  43. 3:00where there was lower compliance than we would have liked.
  44. 3:03And here, we can see the changes that it suggested.
  45. 3:05Since our factory is configured as code,
  46. 3:07it's easy for agents to reach into the configuration
  47. 3:10and suggest ways to improve skills.
  48. 3:12And with that, we have a closed loop system.
  49. 3:15Bring tasks into the factory and let the code quality improve on its own.
  50. 3:20If that sounds interesting to you, check out Warp Factories in Early Access.
50 पंक्तियाँ · 635 शब्द

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