agentic-confidence-calibration

name	agentic-confidence-calibration
title	Agentic Confidence Calibration
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.15778
keywords	["confidence-calibration","agent-reliability","trajectory-analysis","overconfidence","process-monitoring"]
description	Diagnose and correct overconfidence failures in autonomous agents using Holistic Trajectory Calibration (HTC), analyzing process-level features across entire execution paths. Use when building reliable autonomous systems that need better confidence estimates and reduced overconfidence failures.

Agentic Confidence Calibration

This skill provides methods to diagnose and improve confidence calibration in autonomous agents by analyzing their entire execution trajectories, addressing overconfidence failures that lead to incorrect decisions despite seeming certainty.

When to Use

• Autonomous agents making irreversible decisions (safety-critical)
• Systems exhibiting overconfidence failures (confident but wrong)
• Building more reliable and trustworthy agent systems
• Debugging agent failures where confidence was misaligned with correctness
• Improving real-world agent deployment safety

When NOT to Use

• Simple inference tasks without long execution trajectories
• Domains where confidence estimates are already well-calibrated
• Systems where collecting trajectory data is expensive
• Low-stakes applications where overconfidence doesn't matter

Key Concept

Overconfidence failures occur when agents express high confidence in incorrect decisions. The problem: agents are often wrong about their own correctness. Agentic Confidence Calibration solves this by analyzing the entire decision trajectory:

• Process Features: Internal reasoning steps, backtracking, error corrections
• Execution Path: How the agent reached its decision
• Holistic Analysis: Patterns indicating overconfidence vs. justified confidence

Instead of trusting superficial confidence scores, examine the trajectory.

Implementation Pattern

Analyze agent trajectories to calibrate confidence:

# Pseudocode for Holistic Trajectory Calibration (HTC)
class TrajectoryCalibration:
    def __init__(self, agent):
        self.agent = agent

    def analyze_trajectory(self, task, agent_execution):
        # Execute task and collect full trajectory
        trajectory = {
            "steps": agent_execution.steps,
            "decisions": agent_execution.decisions,
            "confidence_scores": agent_execution.confidences,
            : agent_execution.backtracks,
            : agent_execution.corrections
        }

        
        features = .extract_trajectory_features(trajectory)
        
        

        
        calibrated_confidence = .calibrate_from_features(
            original_confidence=trajectory[][-],
            trajectory_features=features
        )

         {
            : trajectory[][-],
            : calibrated_confidence,
            : features
        }

     ():
         {
            : (trajectory[]),
            : (trajectory[]),
            : .count_reversals(trajectory),
            : mean(trajectory[]),
            : var(trajectory[]),
            : (trajectory[])
        }

     ():
        
        penalty = (features[] *  +
                  features[] *  +
                  features[] * )

         (, original_confidence - penalty)