Using AI to Close the Gap Between AI-Written Code and Human-Tested Code
Most teams know exactly what AI testing should deliver, but only 16% of QA teams have actually adopted it, even though three out of four call it strategic. The gap is not interest. It is that bolting a general-purpose AI assistant onto your testing workflow produces brittle, throwaway results, and nobody wants to rebuild that wall every quarter.
What if AI capabilities were built natively into your testing platform instead, sharing the same execution context, history, and cluster state your tests already run against?
In this session, Ole Lensmar (CTO, Testkube) walks through Testkube AI, the native AI layer of the Testkube platform, and shows where AI genuinely moves the needle across the testing lifecycle. You will see live demos of autonomous agents that select what to run before execution and triage failures, flakiness, and remediation after it.
During the webinar, we will show how to:
- Spot the two real testing bottlenecks: choosing what to run, and handling what fails
- Use autonomous agents to triage failures and separate flaky tests from real bugs without manual review
- Generate runnable tests from plain-language intent, in the framework your team already uses
- Connect test execution context to any AI tool through an open MCP server
- Evaluate where AI fits your own pipeline, and where it does not
By the end of the session, you will understand why native AI capabilities outperform bolted-on assistants, and how AI-driven engineering teams are closing the gap between how fast code is written and how fast it can be verified.
Learn more about Testkube here.
Ole is the CTO of Testkube, the Open Testing Platform for AI-Driven Engineering Teams, and the host of the Cloud Native Testing podcast. He’s been building testing/QA solutions for 20+ years and is the creator of SoapUI and former steward of the Swagger / OpenAPI Specification. He plays guitar, rides bikes, and goes to the cinema – but rarely all at the same time.
Learn how AI-assisted testing helps teams validate and improve AI-generated code with greater speed, quality, and confidence.
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