Aug 2026· TH Wildau Engineering and Natural Sciences Proceedings· 0 citations· 7 references
TL;DR
This paper examines the phase transition from deterministic algorithmic execution (DevOps) to probabilistic socio-technical orchestration (AgentOps) and synthesizes evolutionary biology and Hellenistic philosophy to reframe human-agent teaming as the integration of a synthetic symbiote.
Abstract
We persist in the delusion that artificial intelligence is merely a sophisticated tool. This is a category error. A tool waits for kinetic energy; an autonomous agent perceives, plans, and acts. The advent of agentic AI proves we are not engineering independent digital workers but structurally embedding ourselves in a higher-order cognitive loop. This paper examines the phase transition from deterministic algorithmic execution (DevOps) to probabilistic socio-technical orchestration (AgentOps). By stripping value creation down to its thermodynamic first principles, we demonstrate that economic advantage no longer resides in process execution. Instead, as we build systems that reason the mechanics of the world, humans are elevated to a harder task: reasoning the beauty, meaning, and axiological value of the world. We synthesize evolutionary biology (Computational Symbiogenesis) and Hellenistic philosophy (Virtue Epistemology) to reframe human-agent teaming as the integration of a synthetic symbiote. Furthermore, we address the steel-manned counterargument of "Epistemic Deskilling" by detailing a taxonomy of "Friction-by-Design." To operationalize this resilience, we deeply expand the mathematical framework for calculating Epistemic Calibration by integrating Bayesian Signal Detection Theory. Finally, we expose the hidden second-order effect: the hyper-commoditization of executive function, which paradoxically makes human liability the scarcest asset in the modern economy.
An interdisciplinary perspective is adopted to examine the paradigm of agentic AI, tracing its evolution from earlier forms of automation and outlining its defining characteristics, architectures and application domains, and addressing the ethical, legal and governance challenges raised by autonomous agents.
Valerio Cencig, Mario D’Almo· Journal of Emerging Perspect...· 0 citations
For our Special Interest Group (S.I.G.), we propose papers that address the orchestration of teams by synthesizing their workflows into a coherent whole, whether these teams are composed of human, machine, Generative AI (gen-AI), robot or AI-Agentic members. The bigger picture of interdependence, teamwork and Gen-AI indicates the need by organizations to build a library of human and artificial agents with bidirectional agency (responsibility) to achieve operational goals (missions), considering agentic risk tolerances, available skills, and vulnerabilities across a complex trade space among the skills available versus those needed for the tasks assigned to complete an operation. In this trade space, agents (human or artificial) from multiple systems with the requisite skills to accomplish a designated task and timeline combined to form a hierarchy of humans, robots, machines and AI. This complex system produces workflows that must be synthesized into a unit(s), then orchestrated to accomplish the goals assigned to it, yet remain trusted even in competitive and uncertain environments. Once synthesized into a unit (e.g., a team), Gen-AI provides the opportunity to not only advance the science of teams by orchestrating team products and performances, but also has raised several concerns (viz., AI used for deception, superintelligence, blackmail, or existential threats to humans). For our S.I.G., We are interested in orchestrating teams: What are the benefits, drawbacks, and, most importantly, can humans, machines and Agentic AI be synthesized and managed (orchestrated)?
W. Lawless, Marco Brambilla, Stephen Russell· AHFE International· 0 citations
Artificial Intelligence (AI) enables powerful capabilities that are transforming almost all sectors. However, the economic growth driven by AI comes at a cost, and its sociotechnical impacts are fraught with contradictions and paradoxes. As a result, several legal initiatives and risk management frameworks have been introduced to mitigate the various risks associated with AI systems. Agentic AI systems require even closer attention than traditional AI. While traditional AI has a narrow focus and responds to direct commands, Agentic AI emerges from combining multiple types of AI capable of planning, tool use, and multi-step execution. These systems can behave and interact autonomously, making decisions and performing tasks to achieve system objectives with minimal human oversight. Recognizing that Agentic AI represents a paradigm shift, this paper addresses its challenges from a Human-AI Interaction perspective. It examines the root causes and impacts of risks arising from the transition from Task Automation to Intentionality Automation, where the user manages outcomes and constraints rather than individual task steps. Key issues include the Open-Loop Control Gap and the Metacognitive Gap, whose relationship is fundamental to understanding the collapse of human oversight, as they represent two sides of the same coin in the loss of control. By analysing scenarios such as cybersecurity and healthcare, this paper identifies dimensions of user demand and identifies Ecological Interface Design as an ergonomic approach to ensure that as AI gains agency, the human retains authority and situational awareness.
M. Simões-Marques· AHFE International· 0 citations
By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment.
Zuo-Jun Max Shen, Yuan Qu, Pujun Zhang et al.· 0 citations
This comprehensive review paper examines the ontological shift in Open Source Intelligence (OSINT). For decades, the field was strictly defined by the extraction of actionable intelligence from publicly accessible data, operating on a linear, highly manual pipeline that relied entirely on human cognitive processing. The advent of Agentic Artificial Intelligence (AI) fundamentally displaces this foundational paradigm. By transitioning the discipline from reactive data aggregation to proactive, autonomous execution, agentic architectures redefine the intelligence lifecycle, shifting the cognitive burden of data processing, correlation, and initial synthesis from human operators to autonomous algorithmic systems. To clarify the academic boundaries and methodological intent of this document, it must be explicitly stated that this is a comprehensive review paper rather than a presentation of singular novel empirical research. The objective of this review is to systematically aggregate, synthesize, and critically evaluate the theoretical, mathematical, and architectural state-of-the-art across the rapidly expanding domain of Agentic AI in cybersecurity and OSINT. By analyzing recent academic frameworks published across leading repositories—such as the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computing Machinery (ACM), Springer, and Elsevier—this paper provides a definitive structural analysis of the current landscape. This review specifically addresses the mathematical foundations of autonomous reasoning through Bayesian probabilistic updating, constructs structural models of multi-agent collaborative topologies, evaluates the operational efficacy of specialized cybersecurity frameworks, and thoroughly examines the profound socio-technical friction generated by this paradigm shift, specifically focusing on the barrier of algorithmic trust, legal accountability deficits, and the emerging defensive imperative to track Non-Human Identities (NHIs).
Arunanshu Chatterjee, A. Roy· Interdisciplinary Journal of...· 0 citations
A five-dimensional diagnostic framework that maps the challenges of human-AI collaboration across Integration, Representation, Scale, Temporality, and Adequacy gaps and shows that augmentation remains the dominant and most viable mode of use in complex environments.
Ganesh Sankaran, Marco A. Palomino, G. Siestrup· Big Data and Cognitive Compu...· 0 citations