Quality Assurance for Agentic AI – Measuring, Understanding, Trusting

The rise of Agentic AI introduces new challenges for quality assurance. These systems don’t just execute commands—they understand tasks, plan actions, choose tools, and operate autonomously in complex workflows. Quality now extends beyond output correctness to include task understanding, decision traceability, and process reliability. This session shows how classic software quality principles—traceability, robustness, efficiency—apply to agentic AI. It presents a framework for assessing and monitoring performance, goal achievement, and trustworthiness across platforms, bridging traditional QA and the demands of autonomous AI systems.

Problems addressed:
The presentation addresses the challenge of ensuring and measuring the quality, transparency, and reliability of autonomously acting Agentic AI systems that plan and decide on their own.

Talk language: English
Level: Newcomer
Target group: Architects & Quality Assurance Managers

Company:
CROWDCODE GmbH & Co. KG

Ingo Düppe