It is argued 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.
Abstract
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.
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.
Software systems have traditionally been organized around applications where human users act as principal decision-makers. Recent developments in agentic capabilities alter this paradigm: software agents now autonomously translate high-level goals into structured tasks, orchestrating tools, services and sub-agents to execute complex workflows. This evolution gives rise to an agent economy where these autonomous agents capture real economic value. However, the infrastructure required to support the agent economy fails across three critical dimensions: the absence of persistent identity infrastructure prevents systemic accountability in agentic workflows; capability claims remain self-declared not backed by verifiable execution evidence; and the disconnect between creator identities, agent performance, and project value hinders the economic valuation of agents as assets. While existing registries provide naming and discovery, unifying these features around a persistent identity anchor remains largely unaddressed. TessIndex is a capability-verified identity system for agent primitives that utilizes a dual-plane architecture: the blockchain records compact commitments for identity, ownership, and verification, while centralized servers maintain dynamic metadata for discovery, commerce, and reputation. It establishes: persistent identities across agent primitives to enforce systemic accountability in autonomous workflows; a predicate-based verification process replacing self-declared claims with cryptographic capability proof; an identity infrastructure that links agent performance to both project and creator identities while capturing value through tokenization. Ultimately, TessIndex serves as an integrated infrastructure that binds an agent's existence across capabilities, execution, and reputation into a single persistent identity.
UMA, a Unified Multi-Agent Framework for enterprise AI systems, is introduced, designed to support the complete lifecycle of agentic systems, including deployment, orchestration, execution, monitoring, and return-on-investment (ROI) realization.
Umamaheswara Rao Kukkala· International Journal of Inn...· 0 citations
The rapid diffusion of data‑driven automation and agentic AI systems is reshaping the foundations of work, decision‑making, and human–technology interaction. As organizations move toward Society 5.0— Japan’s vision for a human-centered “super smart” society in which cyber-physical intelligence augments human capability across economic and social systems—there is an urgent need for operational architectures that are not only technologically capable but also fundamentally human‑centric. This paper presents an applied model using Intelligent Operations framework that integrates agentic AI, enterprise data fabric, human‑in‑the‑loop governance, and secure multi‑system orchestration, and enterprise digital twins that simulate processes and operational states for context-aware decision support. The result is an adaptive socio‑technical system that enhances human decision‑making rather than replacing it, while simultaneously enabling automation at operational scale.The research builds on fieldwork across finance, supply chain, HR, and complex asset‑intensive environments, where organizational processes are distributed across heterogeneous platforms such as ERP, HCM, workflow systems, enterprise data lakes, RPA tools, and emerging AI orchestration layers. Traditional human‑computer interaction models are insufficient in these environments because workers face fragmented data landscapes, inconsistent process execution, and increasing cognitive load. The proposed Intelligent Operations framework addresses these pain points by introducing an orchestration layer that harmonizes data, interprets context (including real-time insights from digital twin models), and deploys agentic AI workers capable of completing multi‑step tasks across systems.A key contribution of this work is the definition of agentic AI in enterprise socio‑technical ecosystems—AI agents equipped not only with language models and planning capability but also with secure access to enterprise systems through structured patterns such as passthrough APIs, workflow orchestration, Model Context Protocol (MCP), and agent‑to‑agent (A2A) collaboration. Rather than relying on brittle rule‑based workflows, the agents dynamically interpret goals, assess context, and plan actionable sequences while maintaining traceability, decision lineage, and auditability. This supports a new form of “digital labor” that works alongside human employees to augment cognitive, administrative, and analytical tasks. However, the framework insists on human‑in‑the‑loop governance, recognizing that human oversight remains essential for ethical, safe, and responsible AI deployment. The DMO acts as a security and compliance boundary—enforcing identity controls, audit trails, approval checkpoints, policy enforcement, and anomaly detection throughout the agentic automation lifecycle. This hybrid model ensures that automation amplifies human capability without bypassing institutional safeguards or creating new forms of risk.The paper also discusses the human‑centric business implications: reduced cognitive load for knowledge workers, increased transparency of decision processes, improvements in cross‑functional collaboration, and the redefinition of roles as humans transition from transactional executors to supervisors, interpreters, and strategic actors. Proposed framework becomes the backbone for Society 5.0 organizational design—linking people, processes, data, and intelligent systems through a unified operational fabric.This research demonstrates that when designed with ergonomics, human values, and socio‑technical principles at the center, agentic AI become powerful enablers of human‑centric, resilient, and adaptive enterprises.
Elizabeth Koumpan, Laurentiu Gabriel Ghergu, Łukasz Strack et al.· AHFE International· 0 citations
Agentic cloud management is emerging as a practice to automate laborious operations, minimize toil, and improve responsiveness. Despite the rapid development of autonomous management agents, we argue that the fundamental missing piece is a systems foundation to enable safe, effective operations across agents and between agents and human operators. In this paper, we advocate for the need of such a systems foundation and share our efforts on developing CloudWeaver, an agentic management substrate that works across existing cloud-user interfaces and future agent-native interfaces. Specifically, we discuss how CloudWeaver (1) scopes the context of individual agent sessions with local views of cloud resources and (2) coordinates concurrent management operations on shared cloud resources. CloudWeaver offers strong safety guarantees and attributable feedback in the presence of conflicting intents, while preserving concurrency between independent operations. We validate CloudWeaver using a representative Azure API workload.
Minghao Li, Ziqian Liu, Ziyu Mao et al.· 0 citations
Governance, serverless, and automation facilitate enterprise integration that is adaptive, cost-effective, low-code, and capable of creating dynamic, context-aware, and value-adding workloads. This creates a need for policy-based management that simplifies implementation in multicloud scenarios. Serverless automation, enabled by event-driven workloads and dynamic resource provisioning for short-lived tasks, can operate in multicloud environments without being controlled by any provider. Introducing artificial intelligence facilitates improvements in scheduling and resource optimization. However, these benefits are not well understood, nor are these ecosystems properly managed. What governance is required for such serverless automation in an AI-enable enterprise integration setting? What does governance in serverless environments achieve? These questions are addressed through a structured research-product approach. The concept of serverless automation is first established, then applied to governance and a serverless context.A critical perspective on governance emerges through examination of central concepts and their interplay within a formal control-compliance-risk framework. From a practical angle, governance focuses on preserving trust in business operation and service delivery. This leads to the consideration of a Cloud Automation Control Plane that governs the core events and processes of Cloud Automation on behalf of initiation parties. The enterprise integration layer can be made serverless to minimize code development, improve resilience, and enhance security with AI assistance. The AI inclusion can be exploited for intelligent workload and resource management without compromising the governing principles. These avenues in combination demonstrate that a clear governance model can enable sustainable Cloud AI Automation for the enterprises and the ecosystem.
Sridhar Mahadevan· International journal of com...· 0 citations