Re-architecting BPM Quality Assurance with Agentic AI

Abstract:

Abstract

Business Process Management (BPM) quality assurance is under increasing strain from rising interaction complexity, omnichannel fragmentation, and the operational limits of manual sample-based review. Conventional QA models provide only partial coverage, delayed feedback, and inconsistent evaluator performance across voice, email, chat, and back-office processes. These constraints cannot be solved by incremental scoring automation alone and instead require a shift from retrospective auditing to AI-led quality engineering. This paper proposes a conceptual framework that integrates six design elements: omnichannel evidence capture, context assembly, hybrid evaluator models, agent orchestration, human-in-the-loop oversight, and cross-cutting governance. The framework is further extended through a multi-layer evaluation model, a loop-engineering construct for continuous improvement, and a five-stage enterprise maturity model for adoption sequencing. The paper’s central contribution is a reusable design theory for treating service quality as an engineered, governed, and continuously improving system in omnichannel BPM environments.