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Your agent skills should improve themselves

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Durasi: 3:24
Bahasa yang diucapkan: Inggris (Amerika Serikat)
Tontonan: 800
Wajah di layar: 89%
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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
1:00across our team.
1:01Here we've created a code quality agent that reviews all the conversations we have working
1:05on our main repositories, and it evaluates whether it has passed or failed our code quality
1:11test.
1:12And we can see the configuration for that here, where whenever an agent is implementing
1:17code 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
1:26agents write, and it's also going to look into the diffs and documentation to suggest
1:32improvements.
1:32And here we've set the model that's going to judge all these conversations, along with
1:36classifications, which are basically a rubric from zero to one where you can define how
1:42the 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
2:24fail next time. And we can see samples of all of those improvement runs right here in our
2:28self-improvement dashboard. This is also featured as baked into warp factories. We can see here
2:34that a self-improvement agent reviewed all of those scored conversations and made some PRs to
2:40actually improve our skills. In this example, our code review agent wasn't fetching all of the
2:44necessary PR feedback before writing a review.
2:48And down here, we can see direct references
2:50to the failing scores that prompted this PR.
2:53Here, it was prompted by our efficiency and task compliance
2:57scores, which are other rubrics that we have set up,
3:00where there was lower compliance than we would have liked.
3:03And here, we can see the changes that it suggested.
3:05Since our factory is configured as code,
3:07it's easy for agents to reach into the configuration
3:10and suggest ways to improve skills.
3:12And with that, we have a closed loop system.
3:15Bring tasks into the factory and let the code quality improve on its own.
3:20If that sounds interesting to you, check out Warp Factories in Early Access.

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