Where agentic AI is most valuable in performance testing

At Tricentis Transform in Dallas, three customers took home 2026 Customer Innovation Awards for the Americas: Wolters Kluwer, the Georgia Department of Revenue, and ADP. Here’s how they scaled quality.

Browser-based automation is gaining traction in testing, but it reads only the UI. Most SAP and Oracle transactions happen off screen, in backend tables no browser tool can reach. Here’s why ERP testing needs governed, end-to-end validation.

Most SAP teams still decide what to test the way they did twenty years ago: run the full regression suite and hope it holds. SeaLights for ABAP brings continuous quality intelligence to SAP QA teams.

Discover how Sogeti and Tricentis helped a leading manufacturer modernize SAP testing with business assurance, end-to-end automation, and risk-based testing.

AI tools like Claude Code and Playwright have made test generation dramatically faster, but faster test generation isn’t a testing strategy. See why enterprise quality still needs the governance, traceability, and technology coverage no general-purpose coding agent provides.

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.

For decades, integration testing meant validating an API contract: right endpoint, right schema, right status code. But MCP lets AI agents discover and chain tools on their own, so the question is no longer just “did each tool respond correctly?” but “did the agent make the right decisions, in the right order, with the right data?” Here’s how the standard changes integration testing.

Tricentis Transform is coming to Dallas, Singapore, and London in 2026. See the speakers, sessions, and case studies – and find your reason to go.

Manual testing processes are creating costly bottlenecks for government agencies – delaying services and eroding public trust. See how real state and local agencies used automated testing to cut testing time by up to 80%, save millions in QA costs, and deliver faster, more reliable digital services to citizens.

AI is changing what quality looks like, and the tools QA teams rely on need to change with it. This Fall, Tosca’s full suite of AI capabilities comes to on-premises deployments – Agentic Test Automation, AI Chat, and MCP Services, all orchestrated and governed through AI Workspace.

Bad data used to be a back-office problem. But as business users start querying data directly through AI, unverified data gets scaled confidently to decision-makers who have no reason to question it. Your business runs on data the same way it runs on software, so why don’t we test both?

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.