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Your agent skills should improve themselves

Plataforma: YouTube
Duração: 3:24
Idioma falado: inglês (Estados Unidos)
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Transcrição de Your agent skills should improve themselves

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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 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.

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