Debugging Performance With AI

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Profiling JavaScript is mostly easy. However, how do you profile gnarly performance issues? 

In this talk, you’ll learn a practical AI-assisted workflow for finding rendering bottlenecks fast. Using a real-world CSS performance bug, we’ll cover techniques like commit bisection, standalone reproductions, synthetic stress tests, and auto-generated lint rules to prevent regressions. We choose CSS because it is famously difficult to profile -- there are no stack traces, obvious breakpoints, or clear debugging workflows, which is why many rendering bugs go unfixed until users complain that the page feels slow. However, the same methodology can be used in dealing with other performance issues. You’ll also see how to use the Chrome DevTools MCP to give Claude direct access to a live browser session and accelerate investigation without replacing engineering judgment. 

The result is a repeatable process for going from “the page feels slow” to a pinpointed line of CSS in under an hour.

This talk has been presented at Web Engineering Summit 2026, check out the latest edition of this Tech Conference.

Bernie Sumption
Bernie Sumption
6 min
11 Jun, 2026

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Video Summary and Transcription
The speaker discusses the challenges of debugging rendering performance and the potential of using AI to address this issue efficiently. Emphasizes the limitations of current tools in providing detailed insights into rendering performance compared to JavaScript performance. Highlights the laborious and repetitive nature of manual optimization processes in improving rendering speed and the role of AI in automating such tasks. Discussing the importance of AI in simplifying complex tasks and flattening the learning curve, particularly in debugging performance issues. Highlighting the significance of installing Chrome developer tools MCP for effective AI utilization. Introducing techniques such as regression bisection and standalone reproduction for efficient performance issue identification and resolution. Exploring synthetic stress tests as a technique to magnify performance issues for better visibility. Demonstrating the power of creating extreme scenarios, like a page with 100,000 divs, to identify and replicate complex problems. Emphasizing the extensive capabilities of AI in systematically troubleshooting and proactively identifying performance bottlenecks.
Available in Español: Depuración de Rendimiento Con AI

1. Understanding Rendering Performance Challenges

Short description:

The speaker discusses the challenges of debugging rendering performance and the potential of using AI to address this issue efficiently. Emphasizes the limitations of current tools in providing detailed insights into rendering performance compared to JavaScript performance. Highlights the laborious and repetitive nature of manual optimization processes in improving rendering speed and the role of AI in automating such tasks.

I work for AG Grid, but I'm not going to be talking about the grid today. I'm going to be talking about a rabbit hole that I disappeared down for several months. Debugging rendering performance is really hard, and I wanted to try and figure out how to get AI to do that for me. And I've been trying to think about what to cover because it's just a seven-minute talk here, and what I think I've got time for. I'm not going to be able to walk you through the whole process, but I want to inspire you with some things that you might not have realized were possible, of ways that you can get AI to tackle this really hard problem for you.

So first of all, why rendering performance is really hard to debug? It's basically hard to debug because the information we get from the tools is not very good. So if you record a performance profile in Chrome here, you click that little button, you interact with your application, and you stop it, and you'll get this thing called a performance flame graph. And the thing that I want to show you in this, so first of all, there's a little video at the top that shows you your interaction. You can pick a bit you're interested in, zoom in on it, and what I want to show you is how good the information is you're getting from the JavaScript section.

It shows you every single function in your application, exactly how long it's spent, and this is why frameworks are so fast. You have this amazing information to optimize that performance. When you're trying to get your rendering performance fast, you don't get any of that. It just tells you how long you spent in each section of the application. And so what we've had to do historically, if we want to make our apps render fast, is basically to do science on our apps. So first of all, you need to learn as much as you can about the rendering process in CSS. Then you basically come up with hypotheses, like reasons why you think your app might be slow, and you test those by editing your app and recording another performance profile and looking at the differences, and it's basically doing science. It's painstaking, it's repetitive, it's time-consuming, and it can be really boring.

2. Leveraging AI for Performance Optimization

Short description:

Discussing the importance of AI in simplifying complex tasks and flattening the learning curve, particularly in debugging performance issues. Highlighting the significance of installing Chrome developer tools MCP for effective AI utilization. Introducing techniques such as regression bisection and standalone reproduction for efficient performance issue identification and resolution.

So there is a Chrome developer tools MCP, which if you install, it can do anything that you can do in that UI. It can click through your page, record performance traces, and analyze for them. And the most important thing that AI can do is to flatten the learning curve. So it used to be that you needed to be an expert to get results. Now you can basically start doing this and get AI to take you through the process and learn as you go. So if there's only two things that you take away from this, I want it to be these two things. First of all, in order for all of this to work, you have to install the Chrome developer tools MCP. Without that, Claude doesn't know what it doesn't know. It won't recommend that you do this, it'll just be ignorant. So you need to do that yourself. And then my big suggestion is, ask the AI what your options are. Don't say fix this performance problem, don't even say why is it slow, say explain to me my options. And that's how you become an expert. And that's how you learn and build up a toolbox of techniques.

So I'm going to show you a few of these techniques now. Again, we're just going to be going through them quite quickly. But the idea is to show you what's possible and just remember anything in here, you can get AI to explain to you and then do it next week. So the first is a regression bisection. So a regression is a special case of a performance problem where you have a version of your application that you know is good and a version that you know is bad. And you can automatically find out exactly, like you can ask Claude, find the commit that introduced this issue and it will do a thing called a git bisection for you. So if you know 50 commits ago, your app was good, now it has this problem, you can say find the commit that introduced it and it will check out a commit in the middle and test it, as in it will check out the source code, build your application, open it, start the dev server, open it in Chrome, navigate to the page, do the action that you told it was slow, record the performance profile, find out whether or not it's slow, really time consuming stuff. And let's say, OK, we figured out commit 25 was good, so it will repeat the process again and it will go systematically through your whole commit history to find out the exact commit. And I've gone away from a lunch break where I had this doing it for an hour and I came back and it's like, hey, this commit, that's your problem. So another technique I use all the time is a standalone reproduction. So here's the page I showed you earlier that recorded a performance trace from. If I was interested in the resized performance of that grid, there's a whole bunch of other stuff up there that's just noise that's going to pollute the trace. So you can tell Claude, hey, here's a page I'm interested in, just make a standalone version of that that just has the grid. And I asked it to do that and it built this. Now if we're interested in the resized performance, you can make this little button here where you have a little button that just resizes the grid five times. If you record a performance trace while you're doing that, you'll get really clean data.

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