Clinical AgentOps: Runtime Governance for Autonomous AI Healthcare Agents

Kamal Singh Bisht; Raj Kumar

doi:10.36948/ijfmr.2026.v08i04.85225

Clinical AgentOps: Runtime Governance for Autonomous AI Healthcare Agents

Author(s)	Kamal Singh Bisht, Raj Kumar
Country	United States
Abstract	Generative AI is entering clinical practice not as a predictor but as an actor. Contemporary healthcare deployments increasingly involve agents: language-model systems that plan over multiple steps,retrieve patient context, invoke tools, write to the electronic health record, and coordinate with otheragents. Health AI governance, however, remains overwhelmingly design-time. Premarket review,transparency labels, and reporting standards evaluate a model artifact under the assumption that behavior is a stable property of that artifact. Agentic systems violate this assumption: their effective behavioris constituted at runtime by the composition of instructions, retrieved context, tool affordances, mem ory, and inter-agent interaction, none of which is fixed at approval time. This paper proposes ClinicalAgentOps, a framework that relocates governance from the artifact into the agent’s execution path. Itcontributes an explicit argument from the premises of design-time assurance to the necessity of inpath control, stated with its falsifying conditions; a runtime failure taxonomy for clinical agents inwhich the unit of analysis is the action trajectory rather than the input–output pair; a two-dimensionalmodel treating autonomy as a graduated, revocable, per-action grant indexed by a Clinical Action RiskTier, with a stated derivation rule from which the minimum control set for each (tier, autonomy) pairfollows; a five-plane reference architecture spanning authorization, execution, observation, assurance,and accountability; a clinical agent trace schema extending emerging generative-AI telemetry conventions with attribution, evidence, and oversight attributes, together with governance metrics computablefrom it; and a mapping from framework components to obligations under prevailing risk-management, privacy, and medical-device regimes. This is a framework and position paper: claims about controlefficacy are advanced as falsifiable hypotheses with the study designs that would test them, not asresults
Keywords	Agentic AI, AIGovernance, Clinical Decision Support, Runtime Assurance, LLMObservability, Health Informatics, AI Safety, Human Oversight, Electronic Health Records (EHR), Medical Device Regula tion
Field	Medical / Pharmacy
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-06
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85225

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Clinical AgentOps: Runtime Governance for Autonomous AI Healthcare Agents

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