Cognitive Orchestration for Enterprise Autonomy
Keywords:
Trust-Aware Decision Making, Autonomous Systems, Enterprise Service Ecosystems, Cognitive Orchestration, Trust Metrics, Context-Aware Intelligence, Service-Oriented Architecture (SOA), Decision Intelligence, Uncertainty Modeling, Autonomous Enterprise Systems.Abstract
Autonomous systems frequently assume that external inputs are fully reliable—an assumption that rarely holds in practice. Absent a robust model of trust and reliability, such systems risk significant failure when operating autonomously. This work examines trust—defined as a subjective perception grounded in assessments of component credibility, reliability, and interpretability—within enterprise service ecosystems: dynamic, scalable environments of semi-autonomous, service-oriented actors that create, consume, and orchestrate services.
Trust-aware psychological models of decision-making under uncertainty address a critical gap in the enterprise architecture autonomy stack. We define qualitative and quantitative trust metrics to support both component selection and the recognition of trustable ignorance—cases where a component’s limitations can still inform sound decisions.
These trust metrics, developed and evaluated through ongoing research, enable enterprise-wide, trust-aware cognitive orchestration and context-grounded decision intelligence. The approach draws on enterprise services, microservices, and event- and message-driven communication paradigms to support trust-aware decision-making in both collaborative and competitive settings. A context taxonomy—encompassing operational, historical, and environmental dimensions—further grounds this work, enabling the selection and abstraction of context needed for context-grounded reasoning and decision-making in autonomous and semi-autonomous systems.
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Data Availability Statement
The data supporting the findings of this study were generated through simulated multi-agent enterprise service ecosystem experiments (180 autonomous service providers, 50 interaction rounds each) and are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 Michael Thompson (Author)
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