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.
NeoLoad 2026.1 brings a more unified and modern NeoLoad Web experience across deployment models, offers cloud capabilities with enhanced security features for teams with strict network requirements, and strengthens governance for how Virtual User Hours (VUH) are managed across complex organizations.
Narwal.ai uses Tricentis to modernize enterprise QA, achieving 81% faster cycles, 95% automation coverage, and scalable continuous testing.
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.
Pre-built Oracle test cases can accelerate automation adoption in the short term. But they don’t eliminate the harder problem: long-term maintenance in environments shaped by continuous SaaS updates, Redwood UI adoption, and expanding integrations. Here’s why resilient, model-based automation matters more than the size of a test library, and the questions Oracle teams should ask when evaluating a platform.
SAP renamed its Integrated Toolchain to the Agent-led toolchain during Sapphire 2026. Learn how Tricentis ECT fits in and what it means for SAP testing teams.
AI initiatives succeed or fail based on data integrity. Learn how continuous enterprise data testing helps prevent bad AI outputs, corrupted analytics, and business risk at scale.
Learn how Accenture and Tricentis help enterprises ship faster with AI-powered automation, GxP-compliant testing, and quality engineering at scale.
Oracle Fusion quarterly updates arrive every three months, and the two-week testing window that follows has long meant a scramble. Agentic AI, self-healing tests, and context-aware agents are giving QA teams a new way to absorb Oracle’s pace of change — without the chaos.
AI agents are transforming how enterprise software works — and ERP systems like SAP are at the center of that shift. Here’s what it means for quality engineering teams responsible for keeping it all running.
Why AI-built apps fail—and how visibility, context, and governance restore trust.
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.
We surveyed more than 2,500 IT and QA leaders across six countries and found a striking paradox: AI adoption is accelerating, but trust in AI to make release decisions is falling.
SAP Sapphire 2026 revealed how agentic AI is transforming the autonomous enterprise, from smarter SAP migrations to intelligent ERP automation.
Capgemini uses Tricentis to modernize banking QA, reducing automation effort by 40–60% and enabling scalable, compliant testing across complex systems.
Conventional test scripts miss how users actually behave. Learn how model-based testing and Business Flows in Tosca help teams build reliable, user-centered tests.
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.
Learn why agentic AI creates accountability gaps in software delivery and how testing, governance, and validation close the risk.
A green pipeline doesn’t mean your code still does what you intended. Learn how a global technology company is using AI Workspace to detect intent drift and bring continuous quality intelligence to AI-assisted development.
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.
The Tricentis Global Partner Awards 2026 recognize the organizations shaping the future of quality engineering and digital transformation. From AI innovation to enterprise-scale delivery, these partners are setting new standards for excellence and impact across industries worldwide.
As AI-generated code accelerates delivery, traditional QA metrics like code coverage and pass rates no longer reflect real risk. Quality intelligence helps teams test what truly matters.
TRIMEDX used Tricentis Tosca and NeoLoad to reduce critical bugs by 50%, improve test execution, and ensure compliance across healthcare systems with faster, more reliable releases.
