165 First, our data aggregator agent pulls in data from multiple manufacturing data systems and detects anomalies. It also handles data cleaning and exception management, laying the foundation for accurate analysis. Then, once the data is clean, our batch conformance agent compares it against expected standards, uses batch profiling to flag inconsistencies and generates compliance summaries to guide downstream decisions. Meanwhile our review management agent makes sure that the right people are looped in at the right time. It triggers review notifications, packages historical quality data and manages approval via application programming interfaces and email workflows. Finally, the quality assurance (QA) assist agent recommends disposition paths – either release, hold or rework – based on the evidence. This is where AI becomes truly assistive and, while human QA still has the final say, the agent ensures decisions are rooted in data and are compliant with standard operating procedures. Let’s return to the question of what does this function become when AI is the decisionmaker, and not just the assistant. With these agents, teams get AI-derived insights and actionable recommendations directly, instead of chasing down documents or manually cross-checking reports. This means that quality escapes and recalls become less frequent as AI agents detect subtle anomalies and ensure every batch meets policy standards. Vikas Hegde is principal and Satish Jha is associate principal at ZS “ These systems don’t just assist, they act” PUBLIC SECTOR Photo: iStock/zorazhuang
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