Adaptive Workflow Intelligence (AWI) is a cognitive architecture for enterprise automation designed to support context-driven decision-making under non-stationary and policy-constrained environments. The architecture is organized around a four-layer Perception–Cognition–Action–Reflection (PCAR) loop, which integrates contextual state construction, hybrid decision-making, workflow execution, and reflection-driven policy evolution. AWI introduces reflection as a first-class mechanism for continuous learning, enabling policies to adapt incrementally based on discrepancies between expected and observed outcomes. The architecture combines rule-based constraints, online learning (e.g., contextual bandits), and optional LLM-based reasoning while preserving enterprise guardrails, auditability, and compliance. The framework is evaluated in a simulated enterprise workflow characterized by delayed feedback and environmental drift. Experimental results show that guardrail-constrained adaptive approaches recover significantly faster than static automation while maintaining full policy compliance. Reflective components further contribute to reduced behavioral oscillation and improved stability under changing conditions. This work provides a structured architectural foundation for building adaptive, interpretable, and policy-compliant enterprise AI systems capable of continual learning in dynamic operational environments.
Sreedevi Pandiyath Viswambaran (Tue,) studied this question.