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This paper introduces the Intention Abstraction Layer (IAL), a middleware designed to enhance the management of autonomous subsystems in industrial environments by representing human intentions as structured, explainable runtime objects. By employing a large language model to parse natural language goals into formal intentions and integrating a consistency monitor to detect conflicts before execution, the IAL enables proactive conflict resolution among competing autonomous agents. The proof of concept demonstrates that the IAL can identify and explain intention conflicts, thereby shifting the focus of behavioral assurance from reactive failure analysis to proactive intention-level checking.
Conflicting intentions among autonomous agents can now be detected and explained before execution, preventing costly operational failures.
Modern industrial environments increasingly run many autonomous subsystems at once - schedulers, energy managers, vehicle fleets - each pursuing its own goals while sharing the same physical resources. Because high-level human intentions are translated into low-level control logic and then discarded, no running component can tell whether it is still doing what was actually intended, and goal conflicts surface only after they have caused a missed target or a shutdown. We propose the Intention Abstraction Layer (IAL), a domainagnostic middleware that represents intentions as first-class, persistent, and explainable runtime objects: a large language model grounded in a formal OWL ontology parses naturallanguage goals into structured intentions, a consistency monitor detects conflicts at registration time, before execution, and a transparency module explains them in natural language. We report a first proof of concept in which two autonomous agents register conflicting production and energy intentions, and the IAL flags and explains the conflict before it reaches the execution layer. The result is a mechanism that shifts behavioral assurance for cooperating autonomous systems from post-hoc failure analysis to pre-execution, intention-level checking.