The excitement around AI agents often focuses on building them, but the real challenge lies in ensuring they work reliably in production. AI quality engineering is emerging as a distinct discipline, addressing issues like hallucination, inconsistent outputs, and unexpected behavior in edge cases. Unlike traditional software testing, AI agents require new approaches: evaluating not just code correctness but also the quality of responses, safety, and alignment with user intent. Teams are developing frameworks for continuous evaluation, using techniques like golden datasets, adversarial testing, and human-in-the-loop review. The commercial stakes are high, as poorly tested agents can lead to user distrust and costly errors. This article highlights the growing importance of this field and offers a starting point for teams looking to implement robust quality assurance for their AI systems. As the technology matures, AI quality engineering will likely become as standard as traditional QA.
As AI agents move to production, quality engineering becomes critical. This piece explores the challenges and emerging practices for testing and validating agent behavior.