At the heart of the vision is a continuous operational cycle the authors describe as Perceive, Reason, Act, and Learn. In this model, a perception module continuously gathers data from the environment through sensors, data streams, or other digital inputs. A cognition layer then analyzes this information, drawing on reasoning mechanisms that may combine symbolic logic, probabilistic inference, and deep neural representations. A decision-making component selects actions aligned with the agent’s objectives, constraints, and context, and feedback loops allow the system to refine its strategies as tasks unfold. Unlike automated scripts that follow fixed instructions, agents in this framework are entities with planned behavior, prioritization, and adaptation, capable of pursuing goals that are either preprogrammed or self-generated without continued human supervision.

The architecture proposed by the team is deliberately layered. At its core sits a large language model serving as the reasoning engine, surrounded by four functional modules covering planning, memory, tools, and action. Around these sit multi-agent scaling patterns, ranging from single agents to distributed, hierarchical, and orchestrated deployments, and finally the external interfaces that connect agents to users, APIs, data sources, cloud platforms, knowledge bases, and the web. Planning infrastructure breaks high-level objectives into practical subtasks, often using reinforcement learning or model-based planning, while memory components store short-term context and retain long-term knowledge. Policy layers enforce operational rules and boundaries. The authors stress that this holistic design is intended to solve a critical problem in the field: the isolated development of components that were never designed to work together.

That isolation problem runs deep. Modern large language models are trained predominantly on publicly available internet content, which gives them broad but shallow competence. When applied to real-world domains such as healthcare, transport, or industrial automation, they often fail to identify domain knowledge, dynamically evolving contextual signals, and operational constraints. The authors call this the jack-of-all-trades but master-of-none effect: generalist models provide a wide base of knowledge but lack depth in any particular area unless they are adapted, fine-tuned, or integrated with external resources and structured databases. Retrieval-augmented generation can mitigate some of these weaknesses by grounding outputs in external information, but only if the retrieval itself is reliable, and it adds computational overhead that strains infrastructure.

Hallucination remains perhaps the most stubborn technical defect. The paper notes that state-of-the-art systems struggle with hallucinations in their core language models, a problem rooted in pre-training restrictions and post-training behavior in which models deliver confident outputs even under ambiguity. The issue is compounded by a training-deployment misalignment: models are typically trained on limited, task-focused datasets rather than actual operational conditions, so performance degrades when the practical environment differs from the training setup. In an autonomous agent, such errors do not merely produce a wrong answer; they can cascade into harmful actions with unclear accountability for who is responsible, raising difficult questions of safety, trust, and ethics.

Scaling introduces its own failure modes. When multiple agents operate within shared environments, their interactions can produce emergent system-level behaviors that are not explicitly programmed but arise from collective dynamics, a hallmark of complex systems. While emergence can be powerful, it also invites coordination failures across distributed systems, and the authors observe that learning methods such as reinforcement, meta-learning, and transfer learning have been developed separately from scaling approaches that move from single-agent to multi-agent configurations, with no common standard for deploying them across different fields. High-level reasoning, planning, and learning operations are also computationally and energy intensive, making them practically unsuitable for real-time, large-scale tasks such as smart city management, and performance decay is observed as the number of agents or tasks grows.

Despite these caveats, the survey of applications is striking in its breadth. In healthcare, agentic AI is being used for autonomous construction and execution of medical image segmentation pipelines, real-time detection of drug-induced liver injury risks from clinical records, AI-assisted interpretation of neuromuscular electrodiagnostic tests, and personalized adaptive fitness coaching built on multimodal multi-agent digital twin systems. Future clinical assistants could integrate records, imaging, wearable devices, and genomic profiles to support continuous monitoring and early disease prediction. In transportation, agents support multimodal model predictive control for autonomous navigation and multi-agent routing optimization in logistics networks, with future systems coordinating vehicles, infrastructure, and public transit to manage congestion proactively.

The industrial and commercial reach is equally broad. In software engineering, agents automate task decomposition, tool selection, and execution with real-time feedback, and generate behavior-driven development test cases from natural language, with the long-term prospect of autonomous assistants managing the entire software lifecycle. In finance and banking, agentic systems underpin risk profiling, LLM-based financial modeling, compliance workflows, fraud detection, and personalized customer service. Smart manufacturing deploys agents for robotic assembly, supply-chain management, human-robot collaboration, and self-managing quality systems, while smart cities apply them to energy optimization, fault detection, and urban service coordination. The authors also document applications in military and security contexts, including swarm decision-making and moving-target defense, alongside multidisciplinary uses in radiology, predictive crime analytics, tourism pricing, and autonomous coding agents.

The challenges catalogued in the paper read like a checklist of everything that could go wrong. Agents may harbor biases that conflict with human values, ethics, and cultural beliefs, particularly dangerous in sensitive domains like medicine and finance. Unpredictable behavior arises from information gaps and interactions among agent components, and it is difficult to authenticate, test, and regulate agent decisions, risking failure cascades in multi-agent systems. Privacy and security risks loom because these systems handle sensitive data and are vulnerable to manipulation by malicious actors. Reliability suffers under noisy or incomplete inputs, and the lack of standardized benchmarks and evaluation methods makes impartial comparison nearly impossible, inviting exaggerated performance claims. Biased, incomplete, or outdated training data can perpetuate social inequalities, and poor coordination among agents in decentralized systems undermines both scalability and dependability.

To chart a way forward, the authors propose a six-dimensional research roadmap. It calls for trustworthy autonomous systems aligned with human values, reasoning frameworks that embed ethical principles and legal regulations, and explainable agents whose planning and decision-making can be verified by users. It urges research into decentralized communication strategies, adaptive task allocation, hierarchical coordination, and efficient collaboration protocols for large-scale multi-agent environments. Continuous adaptation through lifelong learning, meta-learning, and memory-enhanced reasoning would let agents acquire new knowledge without forgetting old lessons, while privacy-preserving techniques such as federated learning, secure multi-party computation, homomorphic encryption, and differential privacy would enable collaborative learning without exposing sensitive information. Finally, the roadmap demands standardized benchmarking datasets, evaluation metrics, testing environments, and validation protocols. The message of the paper is ultimately one of guarded optimism: agentic AI could transform healthcare, industry, cities, and governance, but only if the field abandons isolated component development and builds integrated, accountable, and rigorously evaluated systems fit for the real world.

Subject of Research: Conceptual frameworks, applications, and open challenges of autonomous agentic AI systems built on large language models

Article Title: Agentic AI: Vision and challenges

Article References: Gill, S. S., Murugesan, S. S., Anurag, K. A., Verma, P., Kaur, H., Kumar, S., & Kumar, M. (2026). Agentic AI: Vision and challenges. PLOS Complex Systems, 3(8), e0000120. https://doi.org/10.1371/journal.pcsy.0000120

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000120

Keywords: agentic AI, large language models, autonomous agents, hallucination, multi-agent systems, healthcare AI, smart cities, machine learning, AI ethics, scalability, retrieval-augmented generation, PLOS Complex Systems