Systems | Development | Analytics | API | Testing

If you want to avoid API rate limiting headaches during load testing, start by proactively monitoring usage patterns. Use real-time metrics to catch spikes before they trigger throttling. Tools like LoadFocus make it easier to visualize these patterns and pinpoint problematic clients or endpoints. Review how you structure requests – batching similar calls together and caching predictable responses can cut down on redundant traffic and ease pressure on the API.

Enterprise software organizations allocate most of their total IT budgets to software validation and quality maintenance. According to the Consortium for Information and Software Quality (CISQ), poor software quality drains over $2.41 trillion annually from the US economy in operational failures, technical debt, and unmitigated production incidents.

Your team has more changes to test, more environments to cover and less time to maintain regression suites. AI can help produce tests, but that introduces another workload: deciding whether those tests check the right behavior, whether a repair hides a defect and whether the evidence is enough to support a release. Agentic QA tools aim to take on more of that work, from planning and authoring to execution and failure investigation.

In a release test I published in July, Katalon’s AI runner reported seven cases passed and four failed. I could open the steps, inspect their screenshots, and watch the browser session. The failed rows showed which expectations broke. The passed rows deserve the same scrutiny: what had the agent observed before it called each step passed? An AI test runner has to perform the action and judge the outcome. A false pass is a passed verdict when the expected behavior did not occur.

Not all AI transformations are equal, but when AI works well in quality engineering, the results are hard to ignore: 65% fewer major incidents, AI-generated test volume growing 6-7× in a year, or a five-step manual process replaced by three automated workflows. These are just a few of the outcomes from companies that moved past scattered AI experimentation to quality engineering that runs on a continuous, organization-wide signal.

Before you can answer whether checkout will survive Black Friday, you need a test you can rerun. In this demo, we used a coding agent and the Speedscale skills to record a Node app, mock its downstream API, and run a 30-second load test with 10 virtual users. That’s a small first step in holiday readiness. It gives you a working test and a result to inspect, with time left to fix what you find before a code freeze.