वीडियो पर वापस

Warp

Your agent skills should improve themselves

प्लैटफ़ॉर्म: YouTube
अवधि: 3:24
बोली जाने वाली भाषा: अमेरिकी अंग्रेज़ी
व्यूज़: 2 हज़ार
स्क्रीन पर चेहरा: 91%
गहरे फ़्रेम: 0%

113 में से 42 दिख रहे · 71 ठहरे हुए शॉट्स में समेटे गए

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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।

0:00Let's talk about self-improvement loops for agents. The concept is pretty simple. We have a number of
0:05conversations with our agents every day across our team, and there are a number of failure points in
0:10those conversations where agents may hold the skills wrong or write inefficient code that we
0:15have to go revise, and it would be nice to have a system that's evaluating those conversations as
0:20they happen and suggesting improvements to our skills so that the agent performs better next time.
0:25To address this, we've created a three-step system that works off of all of your conversation data.
0:31It starts from aggregating all of your raw conversations, turning those into scored conversations,
0:37so defining rubrics of what success and failure look like, and then having an agent review all of those scores to suggest improvements.
0:45Here I'll show you what that looks like implemented as part of a larger software factory.
0:50This dashboard is part of Warp Factories, which you can get early access to right now.
0:55And you can see here we've set up a number of scorers that we run across all the conversations across our team.
1:01Here we've created a code quality agent that reviews all the conversations we have working on our main repositories.
1:07And it evaluates whether it has passed or failed our code quality test.
1:12And we can see the configuration for that here.
1:14Where whenever an agent is implementing code on our repositories, we've given it some instructions on how to evaluate the performance.
1:22This code quality agent is going to assess the design and correctness of the code that agents write.
1:27And it's also going to look into the diffs and documentation to suggest improvements.
1:32And here we've set the model that's going to judge all these conversations along with classifications,
1:37which are basically a rubric from zero to one where you can define how the agent should judge that conversation.
1:44For code quality, it's fairly simple.
1:46It's going to be approved if the code is correct and consistent with our conventions.
1:51If there's insufficient evidence, it should bail out without explicitly failing, and it
1:56should block that PR if it finds evidence that it wasn't up to our quality standards.
2:01This agent will run on an automation every time an agent tries to open a PR, and it will
2:06assess the pass or failure over time.
2:08Here as you can see, we've been fine tuning the success and failure criteria to get the
2:12best quality output.
2:14And then, once we have agents grading these conversations, we can have another agent review
2:18all of the runs that failed our quality test and suggest improvements to our skills so they don't fail next time.
2:25And we can see samples of all of those improvement runs right here in our self-improvement dashboard.
2:30This is also a feature that's baked into warp factories.
2:33We can see here that a self-improvement agent reviewed all of those scored conversations and
2:38made some PRs to actually improve our skills. In this example, our code review agent wasn't fetching all of the necessary PR
2:45feedback before writing a review. And down here we can see direct references to the failing scores
2:51that prompted this PR. Here it was prompted by our efficiency and task compliance scorers,
2:58which are other rubrics that we have set up, where there was lower compliance than we would
3:02have liked. And here we can see the changes that is suggested. Since our factory is configured as
3:07code, it's easy for agents to reach into the configuration and suggest ways to improve our
3:12skills. And with that, we have a closed loop system. Bring tasks into the factory and let
3:18the code quality improve on its own. If that sounds interesting to you, check out Warp
3:22Factories in Early Access.

41 पंक्तियाँ · 638 शब्द

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