Jul 2026· Journal of Emerging Perspectives· Vol 2, pp. 87-96· 0 citations· 3 references
TL;DR
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.
Abstract
The evolution of artificial intelligence is redefining the relationship between humans and machines, shifting from traditional automation toward systems endowed with agency. Unlike conventional AI, which primarily supports or executes predefined tasks, agentic AI systems are capable of autonomous decision-making, proactive goal generation, learning from experience and coordinated action within complex environments. This shift represents not only a technological advancement but an ontological transformation, as machines increasingly operate as cognitive agents rather than passive tools. This article adopts an interdisciplinary perspective to examine the paradigm of agentic AI, tracing its evolution from earlier forms of automation and outlining its defining characteristics, architectures and application domains. It then analyses the implications for work and organisational structures, highlighting how agentic systems reconfigure roles, redistribute cognitive labour and enable new forms of human–machine co-agency. Finally, the paper addresses the ethical, legal and governance challenges raised by autonomous agents, arguing for responsible adoption frameworks that preserve human centrality, accountability and social justice in emerging socio-technical systems.
The emergence of agentic artificial intelligence is transforming the foundations of modern software architecture. Traditional distributed systems were designed around deterministic execution models in which predefined workflows and explicit logic governed system behavior. Agentic AI introduces a fundamentally different paradigm by enabling autonomous entities capable of adaptive decision-making, goal-oriented behavior, and contextual reasoning. While this shift increases flexibility and operational intelligence, it also introduces new forms of uncertainty. Autonomous agents operating simultaneously within distributed environments may produce divergent behaviors, make decisions based on incomplete information, and generate system states that are difficult to predict or control. These characteristics challenge traditional assumptions regarding reliability, coordination, and governance in enterprise systems. This paper introduces the concept of Contract-Bound Autonomy, a new architectural model for balancing autonomy and control in distributed agentic systems. Rather than constraining agents through rigid workflows, the proposed model defines explicit operational boundaries through contracts that specify permissible actions, risk limits, compliance constraints, and expected outcomes. Within these boundaries, agents retain the flexibility to adapt their behavior dynamically. The study develops a conceptual framework for understanding how distributed software systems can integrate autonomous agents while maintaining reliability, observability, and governance. It further examines the implications of contract-driven coordination, runtime enforcement, and boundary-aware decision-making in large-scale architectures. By reframing control as the management of acceptable behavioral space rather than deterministic instruction, this work contributes to the emerging field of agentic software systems and proposes a scalable foundation for trustworthy autonomous computing.
Ilker Kanatli· International Journal of Res...· 0 citations
Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, making them suitable for applications across healthcare, finance, cybersecurity, robotics, education, and enterprise automation. This paper explores the conceptual foundations, architectural principles, and practical applications of Agentic AI while examining the key differences between conventional AI and autonomous agent-based systems. It presents a taxonomy that distinguishes reactive and agentic models, discusses single-agent and multi-agent orchestration frameworks, and analyzes the role of memory, planning, perception, and external tool usage in intelligent decision-making. The study also highlights the ethical, security, governance, and accountability challenges associated with deploying autonomous AI, including issues related to transparency, privacy, bias, reliability, and human oversight. As AI systems become increasingly capable of operating independently within complex environments, establishing robust governance and regulatory frameworks is essential. This paper proposes a comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance. By integrating technical innovation with effective governance strategies, the proposed approach aims to maximize the benefits of Agentic AI while minimizing potential risks. The findings contribute to the growing body of research on trustworthy autonomous systems and provide practical guidance for developing secure, reliable, and human-centered Agentic AI solutions.
Dr. Nitin S. Shrirao, Mr. Dnyaneshwar S. Jadhav, Mithun B. Patil· Recent Trends in Mathematics· 0 citations
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 is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 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
Agentic Artificial Intelligence (AI) represents a paradigm shift from static, task-specific systems to autonomous, goal-directed agents capable of reasoning, planning, learning, and acting with minimal human oversight. This paper synthesizes perspectives from philosophy, cognitive science, and AI to define agency, outline its key properties, and situate it in relation to existing paradigms such as reinforcement learning, symbolic reasoning, Belief–Desire–Intention (BDI) architectures, and embodied cognition. We present a taxonomy of agentic systems along dimensions of autonomy, cognitive capability, modality, and environmental interaction, highlighting current capabilities and limitations, and we critically delimit where such a taxonomy is informative and where a functional, closed-loop analysis of agent behavior must take over. The enabling technologies, including large language models, memory architectures, planning frameworks, tool-use mechanisms, and multimodal embodiment, are reviewed alongside diverse application domains ranging from autonomous research and creative systems to web automation and human–AI collaboration. We analyze safety, alignment, evaluation challenges, and emergent risks, dedicate a section to trustworthiness, privacy, and sustainability by bridging from trustworthy machine learning, verified autonomy, and privacy-preserving learning, and compile a comparative review of prominent benchmarks for assessing agentic behavior. Finally, we outline future research directions, including compositional and modular architectures, cooperative AI, simulation-based safe exploration, data-efficient and developmentally inspired learning, hybrid symbolic–neural systems, and strategies for robust alignment. By providing a comprehensive framework and critical analysis, this work aims to guide the development of agentic AI systems that are not only capable but also safe, trustworthy, and aligned with human values.
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.
Svetlana Meissner· TH Wildau Engineering and Na...· 0 citations