Towards safe and trustworthy agentic AI: foundations, taxonomy, technologies, applications, and future directions
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.