The Agilisium Deviation Management agent combines ML-based classification, AI-assistedroot-cause analysis, GenAI CAPA drafting, and explainable decision support into a single, audit-ready workflow - turning a manual, paper-driven process into a governed, closed-loop system.
Quality teams face slow, inconsistent, and poorly governed deviation handling. Four recurring pain points drive closure delays and audit risk.
The agent combines automated classification, AI-guided investigation, and GenAI authoring - with a human review gate at every stage.
ML-based identification and auto-classificationof deviations into minor, major, or critical categories, with human-in-the-loopreview before finalization.
An AI interface guides root-cause analysis(RCA), surfacing historical, similar deviations and evidence to support investigator decisions.
Generative AI drafts corrective and preventive action (CAPA) narratives and protocol-change documentation for QA review and approval.
Explainable AI outputs give investigators and auditors clear reasoning behind every classification and recommendation.
Native integration with QMS, MES, LIMS, and ERP systems keeps deviation data connected to the systems of record.
Quality and manufacturing teams responsible for GxP deviation management, root-cause investigation, and CAPA closure who need faster, more consistent outcomes without sacrificing compliance.
No. The agent automates classification, evidence gathering, and CAPA drafting, while human review gates keep QA investigators and approvers in control at every stage.
Deviation severity classification, root-cause hypotheses, and draft CAPA and protocol-change documentation, all routed for QA sign-off before finalization.
Explainable AI shows the reasoning behind each classification and recommendation, and every action is linked back to the originating deviation record across QMS, MES, LIMS, and ERP.
