Evaluating Agentic Clinical AI Systems

Most clinical AI evaluation frameworks were designed for static prediction models. A model is trained once, validated on a held-out test set, and summarized with metrics such as AUROC or sensitivity at a fixed threshold. This approach assumes that model behavior is stable and that each prediction is an independent event evaluated under controlled conditions.

Those assumptions are becoming less valid as agentic tools emerge across healthcare workflows. Large language models developed by organizations such as OpenAI, Google DeepMind, and Anthropic have accelerated the development of AI agents across industries. Healthcare organizations are now deploying agent-based systems for triage support, documentation, chart summarization, and clinical decision assistance.

A clinical AI agent is an AI system that performs multi-step reasoning or task execution within clinical workflows, most often using large language models and external tools.

How Evaluation of Clinical AI Agents Differs from Traditional Models

Health agents differ from static models because they are interactive, stateful systems embedded in real clinical workflows. Traditional clinical AI benchmarks focus on isolated predictions, and general LLM benchmarks typically evaluate single-turn reasoning, responding to a single input prompt, possibly with some fixed additional context, under controlled conditions. By contrast, health agents operate across dynamic, multi-step tasks in changing care environments. Evaluating them with static-task benchmarks can systematically underestimate safety, reliability, and governance risks.

The table below contrasts traditional clinical AI evaluation with the additional requirements needed to evaluate agentic systems in real-world healthcare workflows.

Below are emerging use cases for clinical AI agents:

• Extract structured data from clinical notes
• Call external tools to schedule appointments
• Summarize longitudinal health records
• Execute multi-step reasoning chains for diagnoses
• Orchestrate task sequences across EHR systems

Evaluation frameworks used today for agentic clinical AI

The rise of health AI agents has led to a new class of evaluation frameworks designed specifically for multi-step, tool-using, language-enabled systems. Traditional medical AI benchmarks focused on classification or question answering. Agent benchmarks attempt to measure reasoning quality, trajectory reliability, and tool coordination under controlled simulation.

These frameworks are an important step forward but are insufficient on their own for real-world governance. Below is a structured overview of common evaluation approaches used for health agents, with particular attention to the recently published MedAgentBench. In practice, multiple frameworks are used to asses agents.

• Simulation-based frameworks

  Simulation-based evaluation frameworks assess health agents within controlled, synthetic environments designed to approximate real clinical workflows. In these settings, agents interact with synthetic patient records, mock EHR systems, structured APIs, and controlled tool interfaces, enabling systematic testing of multi-step behavior.

  This approach allows teams to measure tool-call accuracy, error recovery, sequential decision quality, and the risk of failure cascades across interaction trajectories. Simulation improves ecological validity compared to static test sets because it evaluates agent behavior over time rather than as isolated predictions. However, simulation remains inherently constrained by simplified data distributions, limited workflow variability, and the absence of real clinician behavior. Strong performance in simulation therefore does not guarantee reliability in production environments.

• Human adjudication frameworks

  For generative clinical agents, structured expert review remains a common evaluation approach. Clinical reviewers assess outputs using criteria such as factual consistency with patient data, completeness of differential diagnoses, harm severity grading, and appropriateness of management suggestions. This method captures nuanced clinical judgment that automated metrics often miss, particularly in narrative reasoning tasks.

  However, human adjudication introduces inter-rater variability, scalability constraints, and reproducibility challenges. Without standardized scoring rubrics, clear documentation, and version-controlled evaluation artifacts, adjudication-based assessments can be difficult to audit and compare over time.

Robust agent evaluation requires measuring tool-use accuracy, error recovery, and sequential decision quality in synthetic clinical environments while validating clinical reasoning quality through expert review.

One of the more notable recent advances in agentic clinical AI evaluation is the MedAgentBench benchmark suite. MedAgentBench includes a simulated, interactive clinical environment designed to evaluate how effectively LLMs operate as agents in realistic healthcare settings, particularly when interacting with EHRs. Developed by Stanford researchers, it embeds human clinical expertise into task design and moves beyond static question–answer evaluation to assess performance on complex, multi-step workflows that more closely reflect real-world clinical practice.

MedAgentBench evaluates agentic clinical AI systems across five core dimensions that reflect how these models perform in realistic, interactive healthcare workflows:

• Multi-step clinical task completion: Whether the agent can successfully execute end-to-end workflows rather than answer isolated questions.
• EHR navigation and data retrieval: The ability to query, filter, and synthesize structured patient data (labs, vitals, medications, diagnoses) through a simulated FHIR interface.
• Tool use and API invocation: Whether the agent correctly selects and calls external tools or functions within the clinical environment.
• Planning and action sequencing: The agent’s capacity to reason about intermediate steps, maintain state, and execute actions in the correct order.
• Clinical reasoning grounded in patient context: Whether decisions are appropriate given the full longitudinal record rather than a single prompt.

A Proposed Multi-Axis Framework for Benchmarking Health Agents

A multi-axis framework recognizes that agentic performance is inherently multidimensional. Evaluating health agents requires assessing task success, clinical correctness, safety and boundary adherence, robustness to distribution shift, subgroup stability, calibration, and reproducibility. Performance along one dimension does not compensate for weaknesses in another, and strengths in aggregate metrics may obscure localized or clinically meaningful risks.

The framework below is designed to be operational rather than purely conceptual. Each axis maps to measurable signals, clearly defined evaluation procedures, and interpretable failure modes. Together, these dimensions support a structured assessment of how a health agent behaves in practice, the conditions under which it remains reliable, and where performance limitations or safety risks may arise.

Benchmarking frameworks for agentic clinical AI are advancing rapidly, but increasingly sophisticated evaluation design does not automatically ensure real-world reliability. Agentic systems introduce feedback loops and interaction effects that static benchmarks alone cannot capture. For healthcare organizations, the core question is not only whether an agent can complete a simulated workflow or achieve a high task success rate, but whether it maintains clinical validity, calibration, safety boundaries, and traceability with varying and evolving clinical scenarios and workflows. At Quantiles, we view agentic clinical AI evaluation as an ongoing discipline grounded in rigorous evaluations, reproducible audit trails, and lifecycle monitoring purpose-built for healthcare.