| 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:
class TrajectoryCalibration:
def __init__(self, agent):
self.agent = agent
def analyze_trajectory(self, task, agent_execution):
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)
