Where agentic AI is most valuable in performance testing

Quick summary: Performance teams can generate tests in minutes, but the analysis still takes hours. Agentic Performance Testing in NeoLoad uses domain-specialized AI agents to deliver a finished analysis from a single request, so engineers can start with the conclusions, rather than the raw data.

Performance testing answers a critical question in the quality engineering lifecycle: will this hold up when real people use it, under real conditions, at real volume? It determines whether the system responds well enough to give customers and employees the experience they expect.

Getting to that answer means testing everything between a user action and a result, covering every layer the application touches: APIs and services, database queries, message queues, connection pools, CPU, and memory. But tests are just the starting point. Each one generates a dense set of data that requires specialized training to read and act on. For most organizations, that specialist is a performance engineer, and the work takes hours.

With 42 percent of code now written or assisted by AI, the amount of software performance teams must handle is growing rapidly, leaving teams to hire more experts, cut corners, or become a bottleneck.

But what if they don’t have to choose?

The analysis bottleneck

By now, many teams are using AI tools to help generate performance tests quickly. But more tests come with more data, which places more work on your experts who must review and synthesize them. The step directly after the tests have run, in analysis, is where performance teams encounter some of their most laborious work.

It can take hours or longer for an expert to review test results, find the most important takeaways, and determine next steps. And as performance teams generate more tests in less time, the analysis step will stand out as a bottleneck.

A better starting point

Agentic Performance Testing’s (APT) capabilities focus on where teams can get the most value – the analysis phase.

A request in AI Chat, the AI interface embedded in NeoLoad Web, kicks off domain-specialized agents built on over 20 years of encoded performance engineering expertise as they carry the analysis end to end.

Teams are left with a verdict and the evidence to support it, including infrastructure health, trend analysis, critical findings ranked by impact, and prioritized action items.

Because AI Chat carries the full context of things like the workspace, project, and run, teams can work through any individual part of the results in plain language without opening another tool.

What teams are telling us

Here’s what we’ve heard from performance teams running APT.

They have a head start on analysis. APT lays out the report and the engineer’s job starts at the findings instead of the raw data.

They have time back on every analysis. Even seasoned performance teams report saving hours per analysis, and the impact snowballs with the complexity of the results.

They have depth that is difficult to reproduce. Customers who have tried to match the output of Agentic Performance Testing with their own AI tools have found it falls short on depth and consistency.

They have a second set of eyes. APT surfaces patterns that experienced engineers missed in their own review.

“The AI tool identified a pattern caused by a backend batch process that we hadn’t noticed on our previous runs. It was firing every 5 minutes, and the tool correlated the backend CPU with a bump in response time. Really cool insight buried in the data!”

— Performance engineering team, global systems integrator

Senior engineers get their time back for optimization and architecture, newer performance engineers produce work that used to require a specialist, and validation workflows scale with software development.

Without analysis, faster test generation only helps your teams do part of the work faster; it moves the bottleneck without really removing it. Cutting analysis time from hours to minutes sets performance teams up to seamlessly shift the way they work from a gate at the end of the software development lifecycle into a faster, more sustainable, and more scalable workflow.

Go inside the build

Join a Q&A with Twan Koot, Tricentis Product Manager for Agentic Performance Testing, on how APT was designed and where it goes next. He’ll cover the thinking behind the design, some of the use cases customers have built with it so far, and the features and capabilities still to come.

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Register here for the webinar