Agents Under the Curve (AUC)

TLDR

• in healthcare often output binary decisions comparable to disease or no disease, which by themselves cannot produce a meaningful AUC.
• AUC remains to be the usual technique to compare risk and detection models in medicine, and it requires continuous scores that allow us rank patients by risk.
• This post describes several practical strategies for converting agentic outputs into continuous scores in order that AUC based comparisons with traditional models remain valid and fair.

Agent and Area Under the Curve Disconnect

Agentic AI systems have gotten increasingly common as they lower the barrier to entry for AI solutions. They accomplish this by leveraging foundational models in order that resources don’t all the time have to be spent on training a custom model from the bottom up or on multiple rounds of fine-tuning.

I noticed that roughly 20–25% of the papers at NeurIPS 2025 were focused on agentic solutions. Agents for medical applications are rising in parallel and gaining popularity. These systems include LLM driven pipelines, retrieval augmented agents, and multi step decision frameworks. They’ll synthesize heterogeneous data, reason step-by-step, and produce contextual recommendations or decisions.

Most of those systems are built to reply questions like “Does this patient have the disease” or “Should we order this test” as an alternative of “What’s the probability that this patient has the disease.” In other words, they have a tendency to provide hard decisions and explanations, not calibrated probabilities.

In contrast, traditional medical risk and detection models are frequently evaluated with the world under the receiver operating characteristic curve or AUC. AUC is deeply embedded in clinical prediction work and is the default metric for comparing models in lots of imaging, risk, and screening studies.

This creates a spot. If our latest models are agentic and decision focused, but our evaluation standards are probability based, we’d like methods that connect the 2. The remainder of this post focuses on what AUC actually needs, why binary outputs usually are not enough, and tips on how to derive continuous scores from agentic frameworks in order that AUC stays usable.

Why AUC Matters and Why Binary Outputs Fail

AUC is usually considered the gold standard metric in medical applications since it handles the imbalance between cases and controls higher than easy accuracy, especially in datasets that reflect real world prevalence.

Accuracy is usually a misleading metric when disease prevalence is low. For instance, breast cancer prevalence in a screening population is roughly 5 in 1000. A model that predicts “no cancer” for each case would still have very high accuracy, however the false negative rate could be unacceptably high. In an actual clinical context, that is clearly a nasty model, despite its accuracy.

AUC measures how well a model separates positive cases from negative cases. It does this by taking a look at a continuous rating for every individual and asking how well these scores rank positives above negatives. This rating based view is why AUC stays useful even when classes are highly imbalanced.

While I noticed great modern work on the intersection of agents and health at NeurIPS, I didn’t see many papers that reported an AUC. I also didn’t see many who compared a brand new agentic approach to an existing or established conventional machine learning or deep learning model using standard metrics. Without this, it’s difficult to calibrate and understand how a lot better these agentic solutions actually are, if in any respect.

Most current agentic outputs don’t lend themselves naturally to obtaining AUCs. With this text, the goal is to propose methods to acquire AUC for agentic systems in order that we are able to start a concrete conversation about performance gains in comparison with previous and existing solutions.

How AUC is computed

To completely understand the gap and appreciate attempts at an answer, we must always review how AUCs are calculated.

Let

• ∈ {0, 1} be the true label
• ∈ ℝ be the model rating for every individual

The ROC curve is built by sweeping a threshold across the complete range of scores and computing

• Sensitivity at each threshold
• Specificity at each threshold

AUC can then be interpreted as

This interpretation only is sensible if the scores contain enough granularity to induce a rating across individuals. In practice, which means we’d like continuous or not less than finely ordered values, not only zeros and ones.

Why binary agentic outputs break AUC

Agentic systems often output only a binary decision. For instance:

• “disease” mapped to 1
• “no disease” mapped to 0

If these are the one possible outputs, then there are only two unique scores. After we sweep thresholds over this set, the ROC curve collapses to at most one nontrivial point plus the trivial endpoints. There is no such thing as a wealthy set of thresholds and no meaningful rating.

On this case, the AUC becomes either undefined or degenerate. It also can’t be fairly in comparison with AUC values from traditional models that output continuous probabilities.

To judge agentic solutions using AUC, we must create a continuous rating that captures how strongly the agent believes that a case is positive.

What we need

To compute an AUC for an agentic system, we’d like a continuous rating that reflects its underlying risk assessment, confidence, or rating. The rating doesn’t must be a superbly calibrated probability. It only needs to supply an ordering across patients that’s consistent with the agent’s internal notion of risk.

Below is an inventory of practical strategies for transforming agentic outputs into such scores.

Methods To Derive Continuous Scores From Agentic Systems

1. Extract internal model log probabilities.
2. Ask the agent to output an explicit probability.
3. Use Monte Carlo repeated sampling to estimate a probability.
4. Convert retrieval similarity scores into risk scores.
5. Train a calibration model on top of agent outputs.
6. Sweep a tunable threshold or configuration contained in the agent to approximate an ROC curve.

Comparison Table

In the subsequent section, each method is described in additional detail, including why it really works, when it’s most appropriate, and what limitations to have in mind.

Method 1. Extract internal model log probabilities

I typically lean toward this method every time I can access the model’s final output layer or token-level log probabilities. Not all APIs expose this information, but after they do, it tends to provide essentially the most reliable and stable rating signal. In my experience, using internal log probabilities often yields behavior closest to that of conventional classifiers, making downstream ROC evaluation each straightforward and robust.

Concept

Many agentic systems depend on a big language model or other differentiable model internally. During decoding, these models compute token level log probabilities. Even when the ultimate output is a binary label, the model still evaluates how likely each option is.

If the agent decides between “disease” and “no disease” as its final final result, we are able to extract:

• log (disease)
• log (no disease)

and define a continuous rating comparable to:

• = log (disease) − log (no disease)

This rating is higher when the model favors the disease label and lower when it favors the no disease label.

Why this works

• Log probabilities are continuous and supply a smooth rating signal.
• They directly encode the model’s preference between outcomes.
• They’re a natural fit for ROC evaluation, since AUC only needs rating, not perfect calibration.

Best for

• Agentic frameworks which are clearly LLM based.
• Situations where you’ve got access to token level log probabilities through the model or API.
• Experiments where you care about precise rating quality.

Caution

• Not all APIs expose log probabilities.
• The values could be sensitive to prompt formatting and output template decisions, so it is vital to maintain these consistent across patients and models.

Method 2. Ask the agent to output a probability

That is the tactic I take advantage of most frequently in practice, and the one I see adopted most regularly in applied agentic systems. It really works with standard APIs and doesn’t require access to model internals. Nevertheless, I even have repeatedly encountered calibration issues. Even when agents are instructed to output probabilities between 0 and 1 (or 0 and 100), the resulting values are sometimes still pseudo-binary, clustering near extremes comparable to above 90% or below 10%, with little representation in between. Meaningful calibration typically requires providing explicit reference examples comparable to illustrating what 0%, 10%, or 20% risk looks like. This nonetheless, adds additional prompt complexity and makes the approach barely more fragile.

Concept

If the agent already produces step-by-step reasoning, we are able to extend the ultimate step to incorporate an estimated probability. For instance, you may instruct the system:

The numeric value on this line becomes the continual rating.

Why this works

• It generates a direct continuous scalar output for every patient.
• It doesn’t require low level access to logits or internal layers.
• It is straightforward to clarify to clinicians, collaborators, or reviewers who expect a numeric probability.

Best for

• Evaluation pipelines where interpretability and communication are vital.
• Settings where you may modify prompts but not the underlying model internals.
• Early stage experiments and prototypes.

Caution

• The returned probability might not be well calibrated without further adjustment.
• Small prompt changes can shift the distribution of probabilities, so prompt design must be fixed before serious evaluation.

Method 3. Use Monte Carlo repeated sampling

That is one other method I even have used to construct a prediction distribution and derive a probability estimate. When enough samples are generated per input, it really works well and provides a tangible sense of uncertainty. The essential drawback is cost: repeated sampling quickly becomes expensive in each time and compute. In practice, I even have used this approach together with Method 2. To try this, we first run repeated sampling to generate an empirical distribution and calibration examples, then switching to direct probability outputs (Method 2) once that range is best established.

Concept

Many agentic systems use stochastic sampling after they reason, retrieve information, or generate text. This randomness could be exploited to estimate an empirical probability.

For every patient:

1. Run the agent on the identical input N times.
2. Count how persistently it predicts disease.
3. Define the rating as

• = (variety of disease predictions) /

This frequency behaves like an estimated probability of disease in response to the agent.

Why this works

• It turns discrete yes or no predictions right into a continuous probability estimate.
• It captures the agent’s internal uncertainty, as reflected in its sampling behavior.
• It doesn’t require log probabilities or special access to the model.

Best for

• Stochastic LLM agents that produce different outputs while you change the random seed or temperature.
• Agentic pipelines that incorporate random decisions in retrieval or planning.
• Scenarios where you wish a conceptually easy probability estimate.

Caution

• Running N repeated inferences per patient increases computation time.
• The variance of the estimate decreases with N, so it’s essential select N large enough for stability but sufficiently small to remain efficient.

Method 4. Convert retrieval similarity scores into risk scores

Concept

Retrieval augmented agents typically query a vector database of past patients, clinical notes, or imaging derived embeddings. The retrieval stage produces similarity scores between the present patient and stored exemplars.

If you’ve got a set of high risk or positive exemplars, you may define a rating comparable to

• = max similarity(, )

where indexes embeddings from known positive cases and similarity is something like cosine similarity.

The more similar the patient is to previously seen positive cases, the upper the rating.

Why this works

• Similarity scores are naturally continuous and infrequently well structured.
• Retrieval quality tends to trace disease patterns if the exemplar set is chosen fastidiously.
• The scoring step exists even when the downstream agent logic makes only a binary decision.

Best for

• Retrieval-augmented-generation (RAG) agents.
• Systems which are explicitly prototype based.
• Situations where embedding and retrieval components are already well tuned.

Caution

• Retrieval similarity may capture only a part of the reasoning that results in the ultimate decision.
• Biases within the embedding space can distort the rating distribution and must be monitored.

Method 5. Train a calibration model on top of agent outputs

Concept

Some agentic systems output structured categories comparable to low, medium, or high risk, or generate explanations that follow a consistent template. These categorical or structured outputs could be converted to continuous scores using a small calibration model.

For instance:

• Encode categories as features.
• Optionally embed textual explanations into vectors.
• Train logistic regression, isotonic regression, or one other easy model to map those features to a risk probability.

The calibration model learns tips on how to assign continuous scores based on how the agent’s outputs correlate with true labels.

Why this works

• It converts coarse or discrete outputs into smooth, usable scores.
• It will possibly improve calibration by aligning scores with observed final result frequencies.
• It’s aligned with established practice, comparable to mapping BI-RADS categories to breast cancer risk.

Best for

• Agents that output risk categories, scores on an internal scale, or structured explanations.
• Clinical workflows where calibrated probabilities are needed for decision support or shared decision making.
• Settings where labeled final result data is obtainable for fitting the calibration model.

Caution

• This approach introduces a second model that have to be documented and maintained.
• It requires enough labeled data to coach and validate the calibration step.

Method 6. Sweep a tunable threshold or configuration contained in the agent

Concept

Some agentic systems expose configuration parameters that control how aggressive or conservative they’re. Examples include:

• A sensitivity or risk tolerance setting.
• The variety of retrieved documents.
• The variety of reasoning steps to perform before making a choice.

If the agent stays strictly binary at each setting, you may treat the configuration parameter as a pseudo threshold:

1. Select several parameter values that range from conservative to aggressive.
2. For every value, run the agent on all patients and record sensitivity and specificity.
3. Plot these operating points to form an approximate ROC curve.
4. Compute the world under this curve as an approximate AUC.

Why this works

• It converts a rigid binary decision system into a group of operating points.
• The resulting curve could be interpreted similarly to a standard ROC curve, although the x axis is controlled not directly through the configuration parameter fairly than a direct rating threshold.
• It’s harking back to decision curve evaluation, which also examines performance across a spread of decision thresholds.

Best for

• Rule based or deterministic agents with tunable configuration parameters.
• Systems where probabilities and logits are inaccessible.
• Scenarios where you care about trade offs between sensitivity and specificity at different operating modes.

Caution

• The resulting AUC is approximate and based on parameter sweeps fairly than direct rating thresholds.
• Interpretation relies on understanding how the parameter affects the underlying decision logic.

Final Thoughts

Agentic systems have gotten central to AI including medical use cases, but their tendency to output hard decisions conflicts with how we traditionally evaluate risk and detection models. AUC remains to be a typical reference point in lots of clinical and research settings, and AUC requires continuous scores that allow meaningful rating of patients.

The methods on this post provide practical ways to bridge the gap. By extracting log probabilities, asking the agent for explicit probabilities, using repeated sampling, exploiting retrieval similarity, training a small calibration model, or sweeping configuration thresholds, we are able to construct continuous scores that respect the agent’s internal behavior and still support rigorous AUC based comparisons.

This keeps latest agentic solutions grounded against established baselines and allows us to guage them using the identical language and methods that clinicians, statisticians, and reviewers already understand. With an AUC, we are able to truly evaluate if the agentic system is adding value.

Relevant Resources