Explainable Agentic AI for Secure and Adaptive Supply Chain Decision Intelligence in Food Service and Financial Systems
DOI:
https://doi.org/10.15662/IJARCST.2026.0903008Keywords:
Agentic Artificial Intelligence, AI Safety, Explainable Artificial Intelligence, Decision Intelligence, Food Service Systems, Financial SystemsAbstract
Cognition-based theories for agentic, explainable AI inform secure and adaptive methods for food service and financial decision technologies. However, the use of cognition-based theories has largely been limited to studying and modelling human vs. AI agentic decision-making behaviour without a clear application scope or objectives based on practical applications and associated needs.
Indeed, no clear strategy or way has yet been proposed or implemented for applying an agentic-based explanation approach in decision intelligence supporting agentic actor repeat operations across diverse application domains. A recently devised framework for AI explanation addressed only a subset of potentially critical domain-specific needs, and it has not yet been tested or applied in a different field, nor any specific area, nor adapted for enhanced security and the inclusion of agentic patterns from cognition. Suitability of forensically annotated, structured deep and predictive decision processes for domain context explanation has not yet been established, nor has an inline explain-by-do-don’t concern-expression capability.
Addressing these two related gaps requires examining particular domain food service decision clusters and flows from a specific competence pattern perspective. Food service cognition-based operational support systems with graphical process overlay have become widespread. A database for securing repeat operational actions over other less autonomy-rich decision domains has also recently been established, capturing executive management and board-level financial decision succession and securing decisions from important event signals for high-fidelity decision-intelligence applications within predictive operations and agentic explanation patterns.
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