Aug 2026· Artificial Intelligence Review· 0 citations
TL;DR
This work 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.
Abstract
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.
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
A comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI is proposed, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance and aims to maximize the benefits of Agentic AI while minimizing potential risks.
Nitin S. Shrirao, Dnyaneshwar S. Jadhav, Sarita B. Patil· Recent Trends in Mathematics· 0 citations
Agentic AI systems that reason, plan, and act on complex goals have advanced rapidly across software engineering, scientific discovery, drug development, healthcare, finance, and social simulation. Across these domains a single failure pattern recurs: current systems can execute tasks competently but often struggle to determine when to act, when to pause, when to change strategy, and when to involve a human. Existing reviews catalog agentic architectures, taxonomies, and limitations, but none specify what capabilities these systems must acquire to support dynamic human-AI collaboration. We address that gap. We define collaborative AI as a class of systems that combine generative exploration with autonomous action and calibrate between them based on context, uncertainty, and task demands. We identify four required capabilities: metacognition, contextual mode-switching, uncertainty-aware action, and adaptive human collaboration. We relate these capabilities to established multi-agent systems foundations, including belief-desire-intention architectures, adjustable autonomy, mixed-initiative interaction, and decentralized decision-theoretic control, while specifying the distinct challenges that LLM-based agents introduce. Across the six domains reviewed here, these gaps appear repeatedly and are not solved by current architectures, which positions collaborative AI as a concrete near-term research objective.
Nalan Karunanayake, Savindu Nanayakkara, Kasun Gayashan Hettihewa et al.· International Journal of Net...· 0 citations
Embodied Artificial Intelligence (Embodied AI) has emerged as a promising paradigm for developing more general and adaptive intelligent systems, emphasizing that intelligence emerges from continuous interaction among perception, cognition, and action in real-world environments. Recent advances increasingly integrate large language models and multimodal learning into embodied agents; however, most existing approaches remain correlation-driven, relying on implicit objectives, task-specific rewards, or prompt-level instructions. As a consequence, intent is rarely represented explicitly, limiting causal coherence, long-horizon consistency, and robust value alignment in open-world settings. In this Review, we synthesize recent progress in Embodied AI and articulate Intent-Driven Embodied Artificial Intelligence (IDEAI) as a system-level organizing framework in which intent functions as an explicit, revisable, and verifiable mediating construct between human goals, environmental constraints, and agent behavior. Building on this synthesis, we propose a four-layer conceptual organization-semantic grounding, concept generation and learning, intent modeling, and value alignment-that clarifies how explicit intent mediates perception, cognition, and action in embodied systems. We analyze how existing techniques address recurring failure modes along the intent-to-execution pipeline and highlight the limitations that arise when intent remains implicit. By making intent explicit, revisable, and value-constrained where such structure is needed, IDEAI supports interpretable decision-making, adaptive task decomposition, and value-consistent behavior in open-ended, human-interactive, and safety-critical embodied domains, providing a unifying perspective for advancing Embodied AI toward robust, socially deployable intelligent systems.
Nanning Zheng· National Science Review· 0 citations
A taxonomy-driven survey of the major cognitive capability gaps that continue to constrain the development of Cognitive AI and outlines a conceptual Adaptive Cognitive Intelligence Architecture (ACIA), providing a foundation for future progress toward Cognitive AI and, ultimately, Artificial General Intelligence (AGI).
Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz et al.· 0 citations
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
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