Performance validation isn’t just the performance team’s job, it’s how you prevent system outages and cloud overspending, two problems agentic AI is making worse. AI agents run around the clock and spike compute unpredictably, and load testing alone can’t catch it. Here’s why the best performance test plans are built in partnership with infrastructure teams.
AI-accelerated development means more code, more releases, and more pressure on performance teams. NeoLoad 2026.2 scales performance validation to match the pace of delivery – with a dedicated DevOps Workspace, native MCP server load testing, automated SAP Fiori frameworks, and faster daily workflows.
Learn how Accenture and Tricentis help enterprises ship faster with AI-powered automation, GxP-compliant testing, and quality engineering at scale.
Tricentis attended Datadog DASH 2026 as a gold sponsor — and booth conversations quickly revealed a recurring blind spot: performance validation. As AI accelerates release cycles, the gap between development and production monitoring is getting harder to ignore.
Explore three real-world NeoLoad MCP use cases showing how enterprises automate performance testing, reduce analysis time, empower non-experts, and accelerate AI-driven workflows with agentic performance testing.
Discover 3 proven ways state and local governments use automated testing to reduce migration risk, ensure data integrity, and deliver reliable citizen services.
Capgemini uses Tricentis to modernize banking QA, reducing automation effort by 40–60% and enabling scalable, compliant testing across complex systems.
Learn how Cognizant uses Tricentis NeoLoad to validate enterprise-grade agentic AI platforms, certify mission-critical utility systems, and cut scripting effort by 50% across 100+ global customers.
AI is not replacing performance engineers. It helps them move faster and ride the next tech wave.
AI is accelerating software development, but its confidence can hide critical performance risks until it’s too late. Traditional testing approaches can’t keep up with the speed and scale of AI-generated code. In this blog, we explore how agentic performance testing shifts validation earlier in the lifecycle, helping teams detect issues before they become costly, release-blocking failures.
