How deeply does AI Agents in Action, Second Edition cover model calibration?

Defines calibration, contrasts implicit signals with verbal claims, and outlines correlation and calibration-curve evaluation without detailing a calibration algorithm.

Model calibration

As explained in AI Agents in Action, Second Edition

Confidence calibration is the degree to which an agent’s expressed or inferred confidence corresponds to the correctness of its output.

Confidence calibration measures whether an agent’s confidence tracks the correctness of its answers. Saying “I’m not sure” is not enough: verbal confidence can differ from signals that better predict correctness. section 10.1.4

Useful signals include token probabilities, retrieval scores, and consistency among evidence. Token-likelihood measures are described as better predictors of correctness than verbal expressions of confidence. section 10.1.4

To evaluate calibration:

Run the same diagnostic queries against two agent configurations; measure correctness, calibration, graceful uncertainty, and average tokens consumed. section 10.4.2
Compare an agent with an empty knowledge graph against the same agent after 20 tasks; assess whether accumulated experience improves calibration. section 10.4.2

Measure calibration across queries

For ten queries of varying difficulty, record confidence and ground-truth correctness. Compute their correlation, plot confidence buckets against actual accuracy, and compare the results with a flat ReAct agent. section 10.5

How AI Agents in Action, Second Edition covers it

Cover of AI Agents in Action, Second Edition

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Also explained in

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Build an AI Agent (From Scratch)Calibration is the agreement between a model's confidence or solvability judgments and the correctness of those judgments.

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Related concepts

metacognitionpart of · Metacognition is the capacity to monitor, evaluate, or regulate one's own cognitive processes.
validationcontrasts with · The process of checking data, behavior, systems, models, or results against defined criteria to assess their correctness, suitability, or acceptability.
benchmarkmeasured by · A benchmark is a defined task, dataset, workload, procedure, or reference measure used to assess or compare performance or capability under specified criteria or conditions.

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action, Second Edition. The section links point to the parts of the book it draws on.