AI Agents Testing: 5 Practices That Will Ensure Quality
Summary
AI agents are being adopted across healthcare, enterprise CRM, and e-commerce but most organizations lack the QA practices needed for safe production use. Traditional deterministic testing fails for agents because they behave probabilistically, drift with new data, and rely heavily on data quality and context. The article highlights industry-specific risks — from clinical-safety and regulatory gaps in healthcare to jagged intelligence, siloed agents, and new fraud surfaces in retail — and common in-house blind spots like silent degradation, context blindness, and adaptation bias. It recommends five testing practices and continuous monitoring, and notes QATestLab offers independent QA services to help teams close these gaps before deployment.
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