The term “agentic AI” has started to appear everywhere, all at once – in research papers, funding pitches, product roadmaps, and policy discussions – giving the impression of a sudden surge. This reflects a convergence of long‑running trends: increasingly capable AI models, growing user frustration with static assistance tools, and mounting pressure on AI developers to demonstrate return on investment by delivering more than just analysis and insight.
As enthusiasm has grown, so too has confusion: the label “agentic” is increasingly applied to nearly any system that chains together models, accesses tools, adds automation, or loops over tasks. While this does reflect some genuine technical progress, it has also blurred important distinctions, making it difficult to tell what truly sets agentic systems apart from conventional AI.
What makes AI “agentic” in healthcare?
An AI tool becomes agentic when it is no longer limited to presenting predictions or recommendations to a healthcare worker, and is instead configured to carry out actions within defined boundaries. Traditional medical AI tools are typically assistive: they flag risks, score images, generate notes, or suggest steps, but a human must decide what happens next and manually trigger the corresponding action. Agentic AI systems, by contrast, are built to execute tasks as part of a workflow. They can plan and execute multi‑step tasks, coordinate with other software systems, monitor outcomes, and adjust their behavior to varied inputs.
This does not necessarily mean unrestrained autonomy. In healthcare settings, agentic AI is generally designed to act within tightly specified constraints, such as:
- escalating a deteriorating patient,
- reallocating clinical resources, or
- triggering predefined interventions.
The defining shift is delegated authority: the ability for AI systems to take direct actions in clinical and operational processes rather than generating advice. To illustrate what delegated authority might look like in practice, consider what these systems are envisaged to do:
- An agentic system might continuously monitor streams of patient data, detect early signs of deterioration, and automatically escalate care pathways under predefined clinical protocols.
- In hospitals, agentic AI could coordinate staffing, bed allocation, and patient flow in real time, adapting within bounded ranges to sudden surges rather than relying on static schedules.
- In procedural settings, agentic systems may assist robotic tools by adjusting trajectories, monitoring safety parameters, or correcting errors faster than human reaction times allow.
- Outside acute care, agentic AI could support long‑term management by adjusting monitoring intensity, triggering follow‑ups, or coordinating care across disconnected systems.
Across these examples, the common feature is an AI system that ingests “live” data, determines the appropriate action to take from within a bounded set of possible actions, executes those actions, and monitors the outcomes.
Governance, safety, and why this is hard
Discussions of agentic AI in healthcare tend to fall back on familiar language about responsibility, oversight, and the need to “keep humans in the loop.” This language is repeated so often that it risks obscuring the real issue. Agentic AI is explicitly defined by its ability to take actions — planning tasks, coordinating systems, and executing decisions within clinical workflows. Yet almost every healthcare AI deployment stipulates that a human must retain final control. This creates an uncomfortable contradiction at the core of the concept, leaving an open design question: if an AI system cannot act without human approval, in what meaningful sense is it agentic at all?
The challenge is less about whether humans should remain involved (they must) and more about defining how authority is shared. Governance for agentic AI is focused on deciding which actions can be safely delegated in advance, under what constraints, and with what safeguards. Moving away from human‑in‑the‑loop as a blanket rule, current research increasingly proposes bounded autonomy: AI systems that are configured to take actions within predefined limits and that are fully auditable after the fact. In healthcare, automated actions must be explicitly designed, transparently constrained, and aligned with clinical accountability.
From aspiration to current reality
Despite the growing visibility of agentic AI, most healthcare systems remain at an early and experimental stage, with active development confined largely to research prototypes, simulated environments, and tightly controlled pilots where AI agents assist with specific tasks but operate under close human supervision. What remains unresolved is less a matter of technical capability than of clinical and societal readiness: how much autonomy should these systems actually be granted in real healthcare settings? How this question is answered — through regulation, clinical governance, and accumulated experience with bounded systems — will determine whether agentic AI fulfils its potential or becomes another term that promised more than it delivered.
Hinostroza Fuentes, V. G., Karim, H. A., Tan, M. J. T., & AlDahoul, N. (2025). AI with agency: a vision for adaptive, efficient, and ethical healthcare [Opinion]. Frontiers in Digital Health, Volume 7 – 2025. https://doi.org/10.3389/fdgth.2025.1600216
Njei, B., Al-Ajlouni, Y. A., Sidney Kanmounye, U., Boateng, S., Loic Nguefang, G., Njei, N., Hamouri, S., & Al-Ajlouni, A. F. (2026). Artificial intelligence agents in healthcare research: A scoping review. PLOS ONE, 21(2), e0342182. https://doi.org/10.1371/journal.pone.0342182
Srinivasu, P. N., Aruna Kumari, G. L., Ahmed, S., & Alhumam, A. (2026). Exploring Agentic AI in Healthcare: A Study on Its Working Mechanism [Original Research]. Frontiers in Medicine, Volume 12. https://doi.org/10.3389/fmed.2025.1753443
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