Introducing Agentic Performance Testing: Performance engineering meets AI speed

Thanks to AI, software today ships faster and with more complexity than ever before, and performance teams that rely on workflows built for a slower era are at risk of falling behind. Reliance on manual steps, niche expertise, and disconnected tools create bottlenecks that add risk to every release.

Tricentis NeoLoad is leading this paradigm shift with AI-powered performance capabilities that close the gap and match the pace of validation to that of modern software delivery.

With AI Chat and Agentic Performance Testing, teams have continuous intelligence embedded directly into performance workflows that can expand throughout the software development lifecycle. Your experts will spend less time on manual analysis, reporting, and repetitive work, and more time addressing performance issues quickly and implementing strategic optimizations.

Kick off performance workflows with AI Chat

AI Chat will make it easier than ever for technical and non-technical users to kick off workflows in NeoLoad.

The addition of embedded AI capabilities takes NeoLoad’s already-intuitive user interface even further with the ability to execute complex performance workflows with simple, natural language prompts. And because AI Chat is context aware, we have even mitigated the challenge of complex prompt engineering.

Rather than switching between multiple large language models (LLMs) and adjusting prompts to provide the right context, NeoLoad users now have access to a feature that will act like an embedded LLM, fully cognizant of baseline NeoLoad requirements that external LLMs aren’t equipped with.

The AI interface connects to agentic workflows and Model Context Protocols (MCPs) to execute specialized tasks automatically. This means the ability to orchestrate skilled performance workflows is no longer limited to a small group of experts and no longer depends on third-party LLMs.

Think of AI Chat as the front door for agentic and MCP-driven operations. This is where you start — with plain-language prompts understood easily by a domain-specialized chat interface.

Agentic Performance Testing: Expert execution at AI velocity

Agentic Performance Testing carries out the specialized tasks that AI Chat initiates. When workflows are triggered, your newest specialty-trained AI teammate executes expert-level tasks autonomously, drawing on more than 20 years of encoded performance engineering expertise.

Before the end of the year, Agentic Performance Testing will support the full performance lifecycle, including workflows across test design, execution, and analysis.

The first workflow developed for Agentic Performance Testing delivers comprehensive, expert-level analysis and stakeholder-ready reports, turning raw test data into clear findings. What once took teams hours now takes minutes; the time spent on results analysis and reporting is cut by up to 95%.

Faster software release cycles lead to mounting performance tests, and each test produces data that needs expert-level analysis before teams can act on it. Agentic Performance Testing removes that bottleneck. Teams can analyze results as fast as they produce them, with the consistency and rigor that manual review struggles to maintain at volume.

In the end, this means that actionable insights will surface immediately, findings will feed back into the delivery cycle faster, and performance will become a driver of velocity rather than a constraint on it.

A platform to orchestrate Agentic Performance Testing and beyond

Agentic Performance Testing is powerful on its own, but it’s also part of something larger. Through Tricentis’ platform for quality engineering, agentic performance workflows operate alongside other AI agents across the quality engineering lifecycle, such as Agentic Test Automation and Agentic Test Creation, and Agentic Quality Intelligence, all governed, auditable, and managed from a single platform.

As your organization scales AI adoption across quality engineering, the intelligence that Agentic Performance Testing brings to performance becomes part of a unified, governed approach to AI-driven quality.

When performance signals flow across these systems, teams get a fuller picture of quality. In practice, this means outputs from Agentic Performance Testing can feed into release decisions, and signals from multiple tools can highlight risks earlier in the release cycle.

With everything in one place, performance engineers can monitor, trigger, and compose workflows that operate across quality assurance boundaries.

Read more: AI Workspace: The new control panel for autonomous quality engineering

Be among the first to see Agentic Performance Testing in action

Join us on March 31 to see how AI Chat and Agentic Performance Testing work together. We’ll also lay out our near- and long-term visions for the future of performance engineering.

Claim your spot early for a first look at the future of performance engineering.

Register here