We ship updates to our Safari-based app regularly, and every update is kind of a gamble with performance. Sometimes a small change tanks rendering speed. Sometimes memory usage spikes. We catch it eventually, but usually after users complain.
I’ve been thinking about setting up automated performance monitoring that runs synthetic user scenarios against WebKit and alerts us when things degrade. The problem is building that from scratch is a lot of work—you need to define realistic scenarios, set baselines, implement the monitoring, handle alerting, and make sure it’s actually catching real problems.
I’m considering using an AI copilot to generate a workflow that does this. Describe the workflow, and it builds the monitoring setup. But I’m wondering: does the AI actually understand performance regressions well enough to generate something that catches real issues? Or am I going to end up spending hours tweaking alerts and fixing false positives?
Performance monitoring from AI-generated workflows actually works better than I expected.
What I did: Described a workflow to the copilot—“run these three user journeys in WebKit, measure rendering time and memory, compare against baseline, alert if metrics exceed thresholds.” It generated a workflow that set up synthetic scenarios, captured performance metrics, stored them, and implemented alerting logic.
Did I need to customize it? Yes. I had to adjust the thresholds because initial defaults were too loose. I added some logic to handle normal variations. But the foundation was solid.
The real advantage is that the copilot structures the workflow correctly. It knows to run scenarios sequentially, capture before/after metrics, implement statistical comparisons. You’re not starting from zero trying to figure out how to architecture this thing.
We’ve been running it for three weeks now, and it caught a regression that would have slipped through. Worth the setup.
Generated monitoring workflows are reasonable but need validation. The AI will create something that runs the scenarios and captures metrics. Whether it catches performance problems depends on how well you define your thresholds and what constitutes a regression for your specific app.
I’d recommend starting with the generated workflow, running it for a week without alerting, just collecting data. Then calibrate the thresholds based on what you actually see. After that, turn on real alerting. This prevents the false positive problem.
The AI-generated approach is viable for performance monitoring because the logic is straightforward—capture metrics, compare against baseline, trigger alerts. Where it gets tricky is defining what matters for your app. Is a 50ms increase in render time a real regression? That depends on your baseline and user expectations.
The generated workflow handles the mechanics well. The nuance—setting appropriate thresholds—that’s on you. Work with actual performance data from production, then tune the workflow based on patterns you observe.