Aug 2026· Scientia. Technology, Science and Society· 0 citations· 30 references
TL;DR
The main conclusion is that practical Agentic IoT depends less on placing an entire agent at one tier than on partitioning perception, memory, reasoning, and action under explicit latency, privacy, reliability, and safety constraints.
Abstract
Autonomous Agentic AI extends conventional Internet of Things (IoT) intelligence from isolated inference toward goal-directed systems that perceive, maintain state, plan, invoke services or actuators, coordinate with other agents, and adapt from feedback. Deploying such capabilities across device, edge, fog, and cloud tiers creates a coupled systems problem: cognitive architecture, organizational topology, workload placement, state management, and trust controls must be designed jointly. This systematic review maps and synthesizes peer-reviewed research on agentic and multi-agent architectures, edge intelligence, computation offloading, and continuum orchestration. A reproducible search of OpenAlex used 20 concept queries and 19 foundational-title lookups, followed by metadata screening and DOI-level Crossref verification. From 765 unique records, 251 entered detailed screening, 155 passed topical and quality criteria, and 153 peer-reviewed studies published between 2008 and 2026 formed the final corpus. The evidence was coded by agent paradigm, continuum tier, orchestration mechanism, evaluation maturity, and primary contribution. Agentic or multi-agent architecture was the largest evidence stream (67 studies; 43.8%), while optimization (82; 53.6%), reinforcement learning (44; 28.8%), and multi-agent reinforcement learning (40; 26.1%) were the most frequent orchestration mechanisms. However, 62 studies (40.5%) relied primarily on simulation, whereas only one (0.7%) reported an operational or pilot deployment. The synthesis yields a four-tier reference architecture with a cross-cutting control and assurance plane, a five-family orchestration taxonomy, and a deployment decision framework. The main conclusion is that practical Agentic IoT depends less on placing an entire agent at one tier than on partitioning perception, memory, reasoning, and action under explicit latency, privacy, reliability, and safety constraints. Standardized benchmarks, stateful-agent migration protocols, interoperable capability descriptions, and longitudinal operational evidence remain urgent research priorities.
Large Language Model (LLM)–based agents are rapidly evolving from passive assistants into autonomous, tool-using, and collaborative systems capable of executing complex, long-horizon tasks across web, software, and physical environments. However, the current literature remains fragmented, with inconsistent terminology, ad hoc architectures, and limited evaluation standards, making it difficult to compare systems or deploy them reliably in real-world settings. This paper presents a unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance. We systematically analyze representative single-agent, tool-augmented, and multi-agent frameworks within this taxonomy, highlighting design trade-offs, capability scaling patterns, and recurring failure modes. Beyond architectural analysis, we review emerging evaluation methodologies that move beyond static benchmarks to assess agent behavior, robustness, grounding, and operational cost in interactive environments. Importantly, the survey emphasizes practical considerations for enterprise and safety-critical deployment, including access control, human-in-the-loop oversight, and policy enforcement. By unifying conceptual foundations with empirical trends and deployment constraints, this work provides a structured roadmap for researchers and practitioners to design, evaluate, and govern next-generation LLM-based agentic systems.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations
The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors. As these agents move from isolated cognitive prototypes into complex distributed workflows, they confront challenges that the Service-Oriented Computing community has studied for more than two decades: composition, interoperability, quality of service, lifecycle management, governance, security, and trust. Yet much of today's agentic AI ecosystem is developing these foundations ad hoc, without the engineering rigour required for dependable enterprise and societal deployment. This paper introduces Agentic Service-Oriented Computing (ASOC) as a new research and practice area concerned with engineering agents as services, orchestrating services through autonomous and semi-autonomous agents, and governing ecosystems of agents and services under constraints of trust, cybersecurity, compliance, performance, and accountability. We articulate six foundational principles of ASOC (harness-ability, composability, lifecycle engineering, trustworthiness by design, goal-driven orchestration, and observability/accountability) and organise a five-dimensional research agenda spanning: (i) agentic services foundations and lifecycle engineering; (ii) composition, orchestration, and interoperability; (iii) governance, observability, and accountability; (iv) security, trust, and risk management; and (v) evaluation, certification, and Agentic QoS. We argue that the Services Computing community is especially well positioned to provide the conceptual and engineering spine for this emerging field, transforming agentic AI from fragmented demonstrations into dependable, service-based systems worthy of human and organisational trust.
Amin Beheshti, Rong N. Chang, B. Benatallah et al.· 0 citations
Network intelligence has largely evolved around logically centralized control and orchestration. Although this model simplifies coordination, it creates a critical dependency on centralized services and limits localized adaptation. This paper presents Agentic-Defined Networking (ADN), an architecture that treats autonomous Artificial Intelligence agents as first-class entities embedded across the network infrastructure. ADN has two defining properties. First, agents perceive local state, maintain beliefs, reason over operator-defined objectives, coordinate with peers, and actuate programmable resources without requiring a persistent central controller in the critical decision path. Second, the mapping between agents and infrastructure is a deployment choice, supporting device-level, cluster-level, and hierarchical configurations. We formalize the ADN agent model and workflow and define an operational framework covering communication, lifecycle management, governance, and security. We evaluate feasibility and scaling through Mininet-AI experiments with topologies of up to 200 switches. The results characterize routing throughput, reasoning latency, fault-mitigation time, and coordination cost under distributed and hierarchical configurations. The study provides initial evidence of feasibility while identifying the challenges that must be addressed before production deployment.
F. Rossi, Paulo Silas Severo De Souza, Diogo Mainart Monteiro et al.· IEEE Access· 0 citations
Experimentation on distributed, heterogeneous computing environments—from edge devices to large-scale cloud platforms—demands orchestration technologies that are both flexible and extensible. Kiso is an open-source framework designed to provision resources and manage complex scientific workflows across the edge-to-cloud continuum. Its architecture unifies infrastructure provisioning, experiment configuration, and reproducible execution, enabling researchers to compose and monitor experiments that span geographically dispersed sites and variable network conditions. Although Kiso was conceived for workflow management—coordinating data-intensive tasks and ensuring reproducibility across dynamic infrastructures—its modular design makes it equally promising for providing reproducible environments for deploying and studying emerging agentic frameworks, where autonomous AI agents require consistent resource provisioning, cross-site communication, and result collection. We describe Kiso’s core capabilities for resource orchestration, experiment lifecycle management, and integration with containerized services, and we outline how these capabilities can support distributed multi-agent systems. In particular, we discuss how its declarative provisioning, extensible task abstractions, and built-in monitoring and output collection provide a natural foundation for experiments in which reasoning agents plan, negotiate, and adapt in real time. This study situates Kiso at the intersection of scientific workflow management and complex, agent-based computing, highlighting its potential to accelerate research on adaptive, self-organizing cyber-physical systems—an emerging frontier in complex systems science.
R. Mayani, K. Vahi, M. Rynge et al.· Frontiers in Complex Systems· 1 citation
Digital twins have emerged as a foundational technology within the context of Industry 4.0, offering a paradigm for the real-time virtual representation of physical systems. However, managing their growing complexity, particularly in distributed industrial environments, requires intelligent architectures capable of autonomous decision-making, dynamic adaptability, and inter-agent coordination. This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts. Through a critical analysis of over 547 papers published in high-impact journals (IEEE Transactions, Nature, Elsevier, MDPI), we establish a taxonomy of existing hybrid architectures, identify persistent technological bottlenecks, and formulate three open research questions concerning: (i) the deployment of artificial intelligence on resource-constrained microcontrollers, (ii) distributed multi-node coordination via lightweight communication protocols, and (iii) the hierarchical orchestration of Digital Twins toward smart factory control integrating residual life estimation and explainable Artificial Intelligence. The results of this analysis reveal that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.
A semantic-uncertainty-guided orchestration approach, HASSUM is introduced as a general framework for uncertainty-aware coordination in multi-agent systems and suggests that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.
John Knowlton, Aritra Guha, Risto Miikkulainen· 0 citations