Confidence Score

Confidence score is a quantitative measure of agent certainty about action correctness or task completion likelihood. In agentic systems, confidence scores provide a numerical assessment—typically ranging from 0.0 to 1.0—that represents how certain an agent is about a specific decision, action, or predicted outcome.

Unlike binary success/failure indicators, confidence scores enable nuanced decision-making about when to proceed autonomously, when to request human confirmation, and when to fail fast. For production agent systems that must balance automation efficiency with safety, confidence scoring is fundamental to implementing adaptive automation strategies that operate safely across varying conditions.

Why It Matters

Confidence scores are critical for operating agent systems safely and efficiently at scale:

Adaptive Automation Strategies: Agents face tasks of varying difficulty and ambiguity. Some tasks are straightforward—clicking a clearly labeled button, filling a form field with provided data. Others are ambiguous—interpreting vague user instructions, selecting among similar UI elements, deciding whether a task has truly completed. Confidence scores enable agents to adapt their behavior: proceeding autonomously for high-confidence decisions while escalating low-confidence cases to human operators or requesting explicit confirmation. Without confidence scoring, agents must either operate fully autonomously (risking errors on ambiguous cases) or always request confirmation (eliminating automation benefits).

Automated Decision-Making Thresholds: Production agent systems require clear policies about when autonomous action is acceptable. Confidence scores provide the quantitative basis for these policies. An agent might be configured to: proceed autonomously when confidence exceeds 0.9, request user confirmation when confidence is between 0.6 and 0.9, and reject the task when confidence falls below 0.6. These thresholds transform subjective judgments about agent reliability into measurable, auditable policies that can be tuned based on empirical performance data.

Escalation and Handoff Logic: Complex agent systems often implement multi-tier architectures where simpler models handle routine cases and more capable (but expensive) models handle difficult cases. Confidence scores drive these routing decisions. When a fast, inexpensive model produces low-confidence predictions, the system escalates to a more powerful model. When even advanced models produce low confidence, the system hands off to human operators. This creates cost-effective hybrid automation that optimizes for both speed and accuracy.

Quality Control and Error Prevention: Confidence scores serve as early warning signals for potential errors. Analysis of agent mistakes typically reveals that errors correlate with low confidence—the agent's uncertainty reflected genuine ambiguity that should have triggered additional validation. By monitoring confidence distributions and flagging low-confidence actions for review, teams can implement quality control processes that catch errors before they impact users or systems.

Calibration and Model Improvement: Well-calibrated confidence scores—where stated confidence matches empirical accuracy—enable data-driven model improvement. When an agent claims 90% confidence but achieves only 70% accuracy on those predictions, the model is overconfident and requires calibration. When confidence scores are well-calibrated, teams can confidently set automation thresholds knowing the real-world implications. Calibration metrics provide objective measures of model trustworthiness.

Concrete Examples

Model Confidence from LLM APIs: Many language model APIs return confidence scores or log probabilities alongside their outputs. When an agent uses an LLM to classify user intent, the model might return:

const response = await llm.classify({
  text: "Can you help me update my billing information?",
  categories: ["account_management", "billing", "technical_support", "general_inquiry"]
});

// Response includes confidence scores per category
{
  predicted_category: "billing",
  confidence: 0.94,
  all_scores: {
    billing: 0.94,
    account_management: 0.78,
    technical_support: 0.12,
    general_inquiry: 0.08
  }
}

The agent uses this 0.94 confidence score to determine it can proceed autonomously. If confidence were 0.65—indicating ambiguity between "billing" and "account_management"—the agent might ask the user to clarify their request.

For generative tasks, log probabilities provide confidence signals:

const completion = await llm.complete({
  prompt: "Generate a professional email declining a meeting invitation",
  return_logprobs: true
});

// Calculate average log probability as confidence proxy
const avgLogProb = completion.logprobs.reduce((sum, lp) => sum + lp, 0) / completion.logprobs.length;
const confidence = Math.exp(avgLogProb); // Convert log prob to probability

if (confidence < 0.7) {
  // Low confidence - review before sending
  await requestHumanReview(completion.text);
}

Heuristic Confidence Scoring: Agents often combine multiple signals into composite confidence scores using heuristic rules:

function calculateActionConfidence(action: AgentAction): number {
  let confidence = 1.0;

  // Reduce confidence based on UI element ambiguity
  if (action.targetElements.length > 1) {
    confidence *= 0.8; // Multiple possible targets
  }

  // Reduce confidence if element lacks clear identifiers
  const element = action.targetElement;
  if (!element.id && !element.ariaLabel && !element.testId) {
    confidence *= 0.85; // Relying on fragile selectors
  }

  // Reduce confidence for newly encountered page structures
  const pageFingerprint = computePageFingerprint(action.page);
  if (!seenBefore(pageFingerprint)) {
    confidence *= 0.9; // Unfamiliar page layout
  }

  // Reduce confidence if previous similar actions failed
  const historicalSuccessRate = getHistoricalSuccessRate(action.type);
  if (historicalSuccessRate < 0.9) {
    confidence *= historicalSuccessRate;
  }

  return confidence;
}

This approach combines multiple risk factors—ambiguous targets, fragile selectors, unfamiliar pages, historical failure rates—into a single confidence metric that reflects overall action reliability.

Ensemble Methods for Robust Confidence: Production systems often use ensemble approaches that combine multiple models or strategies, deriving confidence from agreement:

// Multiple models analyze the same task
const models = [modelA, modelB, modelC];
const predictions = await Promise.all(
  models.map(model => model.predict(task))
);

// High agreement = high confidence
const mostCommonPrediction = mode(predictions.map(p => p.action));
const agreementCount = predictions.filter(
  p => p.action === mostCommonPrediction
).length;

const ensembleConfidence = agreementCount / models.length;
// 3/3 agreement = 1.0 confidence
// 2/3 agreement = 0.67 confidence
// All disagree = 0.33 confidence

if (ensembleConfidence >= 0.67) {
  await executeAction(mostCommonPrediction);
} else {
  await escalateToHuman(task, predictions);
}

When multiple independent models agree on an action, confidence is high. When models disagree, the disagreement signals uncertainty that warrants human review.

Temporal Confidence Degradation: Confidence scores can incorporate temporal factors. Agents operating on dynamic web applications face uncertainty about page structure stability:

interface PageSchema {
  url: string;
  fingerprint: string;
  lastValidated: Date;
  validationFrequency: number; // times validated
}

function getTemporalConfidence(schema: PageSchema): number {
  const daysSinceValidation =
    (Date.now() - schema.lastValidated.getTime()) / (1000 * 60 * 60 * 24);

  // Confidence decays over time
  let confidence = 1.0;

  if (daysSinceValidation > 30) {
    confidence *= 0.7; // Page likely changed
  } else if (daysSinceValidation > 7) {
    confidence *= 0.85;
  }

  // Boost confidence for frequently validated pages
  if (schema.validationFrequency > 100) {
    confidence *= 1.1; // Stable, well-tested page
  }

  return Math.min(confidence, 1.0);
}

This acknowledges that confidence in page structure knowledge degrades as time passes since last validation, reflecting the reality of evolving web applications.

Common Pitfalls

Overconfidence and Miscalibration: The most critical pitfall is accepting poorly calibrated confidence scores at face value. Many machine learning models are systematically overconfident—claiming 95% certainty while achieving only 75% accuracy. This overconfidence leads to overly aggressive automation that causes user-facing errors.

Overconfidence typically stems from:

• Models trained without calibration objectives
• Insufficient training data diversity
• Evaluation on non-representative test sets
• Neural networks trained with cross-entropy loss (known to produce overconfident predictions)

Teams must measure calibration empirically rather than trusting raw model outputs:

// Measure calibration: Do 90% confidence predictions succeed 90% of time?
function measureCalibration(predictions: Prediction[]): CalibrationReport {
  const bins = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0];
  const calibrationData = bins.map(threshold => {
    const inBin = predictions.filter(
      p => p.confidence >= threshold - 0.1 && p.confidence < threshold
    );
    const accuracyInBin = inBin.filter(p => p.correct).length / inBin.length;

    return {
      confidenceBin: threshold,
      predictedAccuracy: threshold,
      actualAccuracy: accuracyInBin,
      calibrationError: Math.abs(threshold - accuracyInBin),
      sampleCount: inBin.length
    };
  });

  const expectedCalibrationError =
    calibrationData.reduce((sum, d) => sum + d.calibrationError * d.sampleCount, 0) /
    predictions.length;

  return { bins: calibrationData, ece: expectedCalibrationError };
}

If calibration analysis reveals systematic bias (e.g., 0.9 confidence actually means 0.7 accuracy), apply calibration corrections before using scores for decision-making.

Ignoring Uncertainty Quantification: Some implementations treat confidence as a binary signal—either "confident enough" or "not confident enough"—without propagating uncertainty through the decision-making process. This loses valuable information.

A more sophisticated approach maintains uncertainty awareness:

// SIMPLISTIC: Binary confidence check
if (confidence > 0.8) {
  executeAction(action);
} else {
  requestHumanReview(action);
}

// BETTER: Uncertainty-aware routing
interface ActionDecision {
  action: Action;
  confidence: number;
  uncertaintyFactors: string[]; // Why is confidence not 1.0?
  riskLevel: "low" | "medium" | "high";
}

function routeAction(decision: ActionDecision) {
  if (decision.confidence > 0.95 && decision.riskLevel === "low") {
    return executeAutonomously(decision.action);
  }

  if (decision.confidence > 0.8 && decision.riskLevel === "low") {
    // Medium confidence - execute but monitor closely
    return executeWithMonitoring(decision.action, {
      captureVideo: true,
      alertOnError: true,
      rollbackOnFailure: true
    });
  }

  if (decision.confidence > 0.6) {
    // Lower confidence - request confirmation with explanation
    return requestConfirmation(decision.action, {
      uncertaintyReasons: decision.uncertaintyFactors,
      suggestedAlternatives: generateAlternatives(decision)
    });
  }

  // Very low confidence - full human takeover
  return escalateToHuman(decision, {
    context: gatherFullContext(),
    explanation: explainUncertainty(decision)
  });
}

This preserves nuance, enabling different handling strategies based on both confidence level and action risk.

Confidence Score Misuse in Training Pipelines: Some teams attempt to filter training data using confidence scores—keeping only high-confidence examples for model training. This creates dangerous feedback loops:

1. Model trained on high-confidence examples
2. Model becomes overconfident (it only sees "easy" examples)
3. Model performs poorly on ambiguous real-world cases
4. New training data filtered by same overconfident model
5. Cycle repeats, progressively narrowing model capabilities

Instead, training data should represent the full distribution of task difficulty, including ambiguous cases where confidence is appropriately low.

Static Thresholds Across All Contexts: Using fixed confidence thresholds (e.g., always require 0.9 confidence) across all task types ignores that different tasks have different risk profiles:

// PROBLEMATIC: One threshold for everything
const CONFIDENCE_THRESHOLD = 0.9;

// BETTER: Risk-adjusted thresholds
const THRESHOLDS = {
  read_only_actions: 0.7,      // Low risk - reading data
  ui_navigation: 0.8,          // Medium risk - navigation
  form_submission: 0.9,        // Higher risk - writing data
  financial_transaction: 0.95, // High risk - money movement
  account_deletion: 0.99       // Critical risk - irreversible
};

function getRequiredConfidence(action: Action): number {
  return THRESHOLDS[action.riskCategory] || 0.9;
}

Thresholds should reflect action consequences. Reading a dashboard requires less confidence than deleting an account.

Neglecting Confidence Score Monitoring: Teams implement confidence scoring but fail to monitor confidence distributions over time. This prevents detection of model drift, data distribution shifts, or degrading performance:

// Track confidence distribution metrics
const metrics = {
  mean_confidence: computeMean(confidences),
  median_confidence: computeMedian(confidences),
  low_confidence_rate: confidences.filter(c => c < 0.6).length / confidences.length,
  confidence_by_task_type: groupBy(predictions, p => p.taskType),
  confidence_vs_actual_accuracy: calibrationAnalysis(predictions)
};

// Alert on distribution shifts
if (metrics.mean_confidence < baseline.mean_confidence - 0.1) {
  alert("Confidence scores dropping - possible model degradation");
}

if (metrics.low_confidence_rate > 0.2) {
  alert("High rate of low-confidence predictions - investigate task difficulty changes");
}

Regular monitoring catches issues before they impact users.

Implementation

Implementing effective confidence scoring requires careful consideration of calculation methods, calibration techniques, and threshold tuning strategies:

Confidence Calculation Methods

Model-Native Scores: For machine learning models, start with native confidence outputs:

// Classification models: softmax probabilities
const logits = model.forward(input);
const probabilities = softmax(logits);
const confidence = Math.max(...probabilities); // Highest class probability

// Regression models: prediction intervals
const prediction = model.predict(input);
const predictionInterval = model.predictInterval(input, alpha=0.05);
const intervalWidth = predictionInterval.upper - predictionInterval.lower;
const confidence = 1.0 - Math.min(intervalWidth / prediction, 1.0);

// Ensemble models: prediction variance
const predictions = ensemble.models.map(m => m.predict(input));
const mean = predictions.reduce((a, b) => a + b) / predictions.length;
const variance = predictions.reduce((sum, p) => sum + Math.pow(p - mean, 2), 0) / predictions.length;
const confidence = 1.0 / (1.0 + variance); // Lower variance = higher confidence

Bayesian Uncertainty Estimation: Bayesian approaches provide principled uncertainty quantification:

// Monte Carlo Dropout: Run model multiple times with dropout enabled
function bayesianConfidence(model: NeuralNetwork, input: Tensor, iterations: number = 50): number {
  const predictions = [];

  for (let i = 0; i < iterations; i++) {
    // Keep dropout active during inference
    const prediction = model.forward(input, { training: true });
    predictions.push(prediction);
  }

  // Confidence from prediction stability
  const mean = computeMean(predictions);
  const std = computeStd(predictions);
  const coefficientOfVariation = std / mean;

  // Lower CV = more consistent predictions = higher confidence
  return 1.0 / (1.0 + coefficientOfVariation);
}

Multi-Signal Composite Scores: Combine multiple confidence signals for robustness:

interface ConfidenceSignals {
  modelConfidence: number;      // From ML model
  heuristicConfidence: number;  // From rule-based checks
  historicalConfidence: number; // From past performance
  ensembleAgreement: number;    // From multiple models
}

function computeCompositeConfidence(signals: ConfidenceSignals): number {
  // Weighted average of signals
  const weights = {
    modelConfidence: 0.4,
    heuristicConfidence: 0.2,
    historicalConfidence: 0.2,
    ensembleAgreement: 0.2
  };

  let composite = 0;
  for (const [signal, value] of Object.entries(signals)) {
    composite += weights[signal] * value;
  }

  // Apply pessimistic adjustment: if any signal is very low, reduce overall
  const minSignal = Math.min(...Object.values(signals));
  if (minSignal < 0.5) {
    composite *= (0.5 + minSignal); // Penalty for very low individual signals
  }

  return composite;
}

Calibration Techniques

Temperature Scaling: Simple post-hoc calibration method that rescales model outputs:

// Train temperature parameter on validation set
function findOptimalTemperature(logits: number[][], labels: number[]): number {
  let bestTemperature = 1.0;
  let bestLoss = Infinity;

  // Grid search over temperature values
  for (let T = 0.1; T <= 5.0; T += 0.1) {
    const calibratedProbs = logits.map(l => softmax(l.map(x => x / T)));
    const loss = negativeLogLikelihood(calibratedProbs, labels);

    if (loss < bestLoss) {
      bestLoss = loss;
      bestTemperature = T;
    }
  }

  return bestTemperature;
}

// Apply temperature scaling during inference
function calibratedConfidence(logits: number[], temperature: number): number {
  const scaledLogits = logits.map(x => x / temperature);
  const probs = softmax(scaledLogits);
  return Math.max(...probs);
}

Temperature > 1 reduces overconfidence by "softening" the probability distribution. Temperature < 1 increases confidence separation (rarely needed).

Platt Scaling: Fits a logistic regression model to calibrate binary classifiers:

// Train calibration on held-out validation set
function trainPlattScaling(uncalibratedScores: number[], labels: boolean[]): PlattScaler {
  // Fit logistic regression: P(y=1) = 1 / (1 + exp(A * score + B))
  const { A, B } = fitLogisticRegression(uncalibratedScores, labels);

  return {
    calibrate: (score: number) => 1.0 / (1.0 + Math.exp(A * score + B))
  };
}

// Apply calibration
const calibrator = trainPlattScaling(validationScores, validationLabels);
const calibratedConfidence = calibrator.calibrate(rawModelScore);

Isotonic Regression: Non-parametric calibration that learns monotonic mapping:

// More flexible than Platt scaling, fits piecewise-constant function
function trainIsotonicRegression(scores: number[], labels: boolean[]): IsotonicRegressor {
  // Sort by score
  const sorted = scores.map((s, i) => ({ score: s, label: labels[i] }))
    .sort((a, b) => a.score - b.score);

  // Fit isotonic regression (monotonic step function)
  const bins = isotonicFit(sorted.map(s => s.score), sorted.map(s => s.label ? 1 : 0));

  return {
    calibrate: (score: number) => interpolateIsotonic(score, bins)
  };
}

Isotonic regression is more flexible than Platt scaling but requires more calibration data.

Threshold Tuning Strategies

ROC-Based Threshold Selection: For binary decisions (autonomous vs. escalate), tune thresholds using ROC analysis:

function selectOptimalThreshold(
  predictions: { confidence: number; correct: boolean }[],
  costFalsePositive: number, // Cost of wrong autonomous action
  costFalseNegative: number  // Cost of unnecessary escalation
): number {
  // Compute ROC curve
  const thresholds = [...new Set(predictions.map(p => p.confidence))].sort();

  let bestThreshold = 0.5;
  let bestCost = Infinity;

  for (const threshold of thresholds) {
    const truePositives = predictions.filter(
      p => p.confidence >= threshold && p.correct
    ).length;
    const falsePositives = predictions.filter(
      p => p.confidence >= threshold && !p.correct
    ).length;
    const falseNegatives = predictions.filter(
      p => p.confidence < threshold && p.correct
    ).length;

    const totalCost =
      falsePositives * costFalsePositive +
      falseNegatives * costFalseNegative;

    if (totalCost < bestCost) {
      bestCost = totalCost;
      bestThreshold = threshold;
    }
  }

  return bestThreshold;
}

// Example: Errors cost 10x more than unnecessary escalations
const threshold = selectOptimalThreshold(validationData, 10, 1);

Multi-Threshold Strategies: Implement multiple thresholds for graduated responses:

interface ThresholdPolicy {
  autonomous: number;      // Above this: fully autonomous
  confirmRequired: number; // Above this: request confirmation
  escalate: number;        // Below this: human takeover
}

function optimizeThresholds(
  validationData: ValidationSample[],
  costs: { error: number; confirmation: number; escalation: number }
): ThresholdPolicy {
  // Grid search over threshold combinations
  let bestPolicy = { autonomous: 0.9, confirmRequired: 0.7, escalate: 0.5 };
  let bestCost = Infinity;

  for (let t1 = 0.95; t1 >= 0.7; t1 -= 0.05) {
    for (let t2 = t1 - 0.1; t2 >= 0.5; t2 -= 0.05) {
      const policy = { autonomous: t1, confirmRequired: t2, escalate: 0.0 };
      const totalCost = evaluatePolicy(validationData, policy, costs);

      if (totalCost < bestCost) {
        bestCost = totalCost;
        bestPolicy = policy;
      }
    }
  }

  return bestPolicy;
}

Adaptive Thresholds: Dynamically adjust thresholds based on context and recent performance:

class AdaptiveThresholdManager {
  private baseThreshold = 0.85;
  private recentErrors: boolean[] = [];
  private windowSize = 100;

  getThreshold(context: TaskContext): number {
    let threshold = this.baseThreshold;

    // Increase threshold if recent error rate is high
    const errorRate = this.recentErrors.filter(e => e).length / this.recentErrors.length;
    if (errorRate > 0.05) {
      threshold += 0.1; // More conservative after errors
    }

    // Adjust based on task risk
    threshold += context.riskLevel * 0.05;

    // Adjust based on time of day (e.g., more conservative during business hours)
    if (isBusinessHours()) {
      threshold += 0.05;
    }

    return Math.min(threshold, 0.99);
  }

  recordOutcome(success: boolean) {
    this.recentErrors.push(!success);
    if (this.recentErrors.length > this.windowSize) {
      this.recentErrors.shift();
    }
  }
}

Key Metrics

Essential metrics for confidence score systems:

Expected Calibration Error (ECE): Measures the difference between predicted confidence and empirical accuracy across confidence bins:

ECE = Σ(|confidence_bin - accuracy_bin| × samples_in_bin) / total_samples

For each confidence bin (e.g., [0.8, 0.9)), calculate the average predicted confidence and the actual accuracy of predictions in that bin. ECE aggregates these differences weighted by bin size.

Target: ECE < 0.05 indicates well-calibrated confidence scores. ECE > 0.15 suggests significant miscalibration requiring correction.

Calculate and monitor:

function calculateECE(predictions: Prediction[], numBins: number = 10): number {
  const binSize = 1.0 / numBins;
  let ece = 0;

  for (let i = 0; i < numBins; i++) {
    const binLower = i * binSize;
    const binUpper = (i + 1) * binSize;

    const inBin = predictions.filter(
      p => p.confidence >= binLower && p.confidence < binUpper
    );

    if (inBin.length === 0) continue;

    const avgConfidence = inBin.reduce((sum, p) => sum + p.confidence, 0) / inBin.length;
    const accuracy = inBin.filter(p => p.correct).length / inBin.length;

    ece += Math.abs(avgConfidence - accuracy) * (inBin.length / predictions.length);
  }

  return ece;
}

Reliability Diagram: Visual representation of calibration showing predicted confidence vs. actual accuracy:

Plot confidence bins on x-axis and actual accuracy on y-axis. Perfect calibration forms a diagonal line (x = y). Deviations reveal systematic biases:

• Points above diagonal: underconfidence (model too cautious)
• Points below diagonal: overconfidence (model too aggressive)

Generate reliability diagrams regularly to monitor calibration drift over time.

Decision Accuracy by Confidence Threshold: Measure how well confidence thresholds predict success:

function evaluateThresholds(predictions: Prediction[]): ThresholdAnalysis {
  const thresholds = [0.5, 0.6, 0.7, 0.8, 0.9, 0.95];

  return thresholds.map(threshold => {
    const aboveThreshold = predictions.filter(p => p.confidence >= threshold);
    const belowThreshold = predictions.filter(p => p.confidence < threshold);

    return {
      threshold,
      countAbove: aboveThreshold.length,
      accuracyAbove: aboveThreshold.filter(p => p.correct).length / aboveThreshold.length,
      countBelow: belowThreshold.length,
      accuracyBelow: belowThreshold.filter(p => p.correct).length / belowThreshold.length,
      separation: accuracyAbove - accuracyBelow // How well threshold discriminates
    };
  });
}

Good confidence scores show strong separation: accuracy above threshold should be significantly higher than accuracy below threshold. Poor separation (< 0.1 difference) indicates confidence scores don't reliably predict success.

Brier Score: Measures the accuracy of probabilistic predictions:

Brier Score = (1/N) × Σ(confidence - actual)²

where actual = 1 for correct predictions, 0 for incorrect predictions. Lower Brier scores indicate better calibrated confidence. Range: 0.0 (perfect) to 1.0 (worst possible).

Target: Brier score < 0.1 for well-calibrated systems.

Confidence-Weighted Accuracy: Measures whether higher confidence correlates with higher accuracy:

function confidenceWeightedAccuracy(predictions: Prediction[]): number {
  const totalWeight = predictions.reduce((sum, p) => sum + p.confidence, 0);
  const weightedCorrect = predictions
    .filter(p => p.correct)
    .reduce((sum, p) => sum + p.confidence, 0);

  return weightedCorrect / totalWeight;
}

Compare confidence-weighted accuracy to unweighted accuracy. If weighted accuracy is significantly lower, confidence scores are misleading (assigning high confidence to incorrect predictions).

Automation Rate vs. Error Rate Trade-off: Track the relationship between automation threshold and resulting error rate:

interface AutomationMetrics {
  threshold: number;
  automationRate: number;  // % of tasks handled autonomously
  errorRate: number;       // % of autonomous tasks that fail
  escalationRate: number;  // % of tasks sent to humans
}

function analyzeAutomationTradeoff(predictions: Prediction[]): AutomationMetrics[] {
  return [0.5, 0.6, 0.7, 0.8, 0.9, 0.95].map(threshold => {
    const automated = predictions.filter(p => p.confidence >= threshold);
    const escalated = predictions.filter(p => p.confidence < threshold);

    return {
      threshold,
      automationRate: automated.length / predictions.length,
      errorRate: automated.filter(p => !p.correct).length / automated.length,
      escalationRate: escalated.length / predictions.length
    };
  });
}

Use this analysis to select thresholds that balance automation benefits against acceptable error rates. Common target: 80%+ automation rate with <2% error rate.

Related Concepts

Understanding confidence scores requires familiarity with several related concepts:

• Observability: Monitoring and tracking confidence score distributions, calibration metrics, and decision outcomes over time
• Task Success Rate: The ultimate metric that confidence scores aim to predict and improve through selective automation
• Guided Mode: Operational mode where confidence scores trigger human confirmations for low-confidence actions
• Failure Modes: Understanding how agents fail helps design confidence scoring systems that detect and prevent common failure patterns

Additional context:

• Calibration: The process of aligning stated confidence with empirical accuracy
• Threshold Tuning: Optimizing confidence thresholds to balance automation rate and error rate
• Uncertainty Quantification: Principled statistical approaches to measuring model confidence
• Human-in-the-Loop: Systems where confidence scores determine when human intervention is required
• Cost-Sensitive Learning: Training approaches that explicitly account for different costs of various error types