AI Agents & the Future of Testing - Szilárd Széll

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"AI is our new workforce. We need to have a hiring process for AI." - Szilárd Széll

In this episode, I speak with Szilárd Széll about the transformative role of AI in software testing and business processes. Szilárd, a notable figure in the testing community, shares valuable insights on the challenges and opportunities that come with integrating AI agents into our workflows. We explore the pressing questions surrounding trust in AI, how it can enhance business agility, and the necessity for testers to adapt their strategies in this evolving landscape. With the rise of AI, we need to rethink our approaches to quality assurance, balancing innovation with caution. As Szilárd suggests, engaging closely with AI can amplify our capabilities and drive progress.

Szilárd Széll is a DevOps Transformation Lead, Test Coach, and SAFe 6.0 SPC at Eficode. He has years of experience with DevOps transformation, especially in the telco industry. He has also worked as an assessor, trainer, facilitator, and coach in test automation and testing process improvement.

Szilard is very involved in the testing community that brought him the Tester of the Year in Finland AWARD 2024 by Tieturi. He runs the Finnish Testing Meetup Group with friends, is active in International Software Testing Qualifications Board (ISTQB) working groups, and is a member of the Hungarian Software Testing Board (HTB). For many years, Szilard has been working on and supporting conferences like HUSTEF, UCAAT, EuroSTAR as PC member or reviewer.

In his personal life, he enjoys kayaking on the sea, playing with LEGO, and being tested by his teenage daughter :-)

Highlights:

• AI agents need a hiring and continuous performance evaluation process, just as human employees go through selection, probation, and ongoing review before being trusted with business tasks.
• Storing every prompt that generates an outcome in a log enables audit trails and statistical analysis, making it possible to trace why an agent's output changed over time.
• Testing AI output can be done by using additional AI models to evaluate results: if a clear majority agree the output is good, it is likely acceptable, and disagreement signals a problem.
• Keeping business process knowledge in-house is a competitive requirement because handing that knowledge to an outside AI provider strips the company of its core differentiator.

More Links with Insights:

• Szilárd Infos