---
title: Your Agent's Confidence Score Is Lying to You
published: false
description: When your LLM says "90% confident," it is right about 60% of the time. If you use uncalibrated confidence for human-in-the-loop routing, you are auto-approving decisions that should be reviewed.
tags: ai, mcp, machinelearning, productivity
canonical_url: 
---

# Your Agent's Confidence Score Is Lying to You

Every production agent system has a confidence threshold somewhere. Above 0.85, auto-approve. Below 0.85, route to a human. It is the simplest and most common pattern for human-in-the-loop AI systems.

There is one problem: the confidence scores are wrong.

## The Calibration Gap

When an LLM reports 90% confidence -- whether through logprobs, self-reported certainty, or chain-of-thought conviction -- it is right about 60% of the time. Sometimes less. The model is not lying. It is miscalibrated. It has no reliable internal mechanism for mapping its uncertainty to a probability that corresponds to real-world accuracy.

This is not a minor discrepancy. It is a 30-percentage-point gap between what the model claims and what actually happens. And it gets worse at the extremes -- exactly where it matters most.

At the high end, when the model says "99% confident," actual accuracy might be 75%. These are the decisions your system auto-approves with the most trust and the least oversight.

At the low end, when the model says "40% confident," actual accuracy might be 55%. These get routed to human review -- wasting expensive human attention on decisions the system is actually handling reasonably well.

The calibration gap means your HITL system is simultaneously too trusting at the top and too cautious at the bottom. The worst of both worlds. This is why OraClaw treats calibration as a tool call, not a prompt instruction — the model cannot self-correct an internal scale it never had.

## What This Looks Like in Production

A customer support agent processes 10,000 tickets per day. The confidence threshold is 0.85: above that, the agent responds automatically; below, a human reviews.

With uncalibrated confidence:

- **7,200 tickets auto-approved** (the model reports > 0.85 confidence)
- **Of those, ~1,800 are actually wrong** (true accuracy at reported 85%+ confidence is only about 75%)
- **2,800 tickets sent to human review**
- **Of those, ~1,400 would have been fine without review** (the model's low confidence was overblown)

The result: 1,800 bad auto-approvals reaching customers + 1,400 unnecessary human reviews consuming agent time. Both failure modes hit the bottom line.

With calibrated confidence:

- **5,500 tickets auto-approved** (after calibration, fewer pass the true 85% accuracy bar)
- **Of those, ~825 are wrong** (calibrated 85% means actual 85%)
- **4,500 tickets sent to human review**
- **Review queue is higher but *meaningful*** -- each reviewed ticket actually needs human judgment

The calibrated system catches 975 additional bad responses per day that the uncalibrated system waved through. At an average cost of $15 per bad customer interaction (support escalation, refund, churn risk), that is $14,625 per day in prevented damage.

## Why Self-Reported Confidence Fails

LLMs learn confidence from training data. When the training data contains phrases like "I am very confident that..." followed by correct statements, the model learns to produce high-confidence language when it pattern-matches to similar contexts. But pattern-matching to the *feeling* of confidence is not the same as *being* calibrated.

There is a second, subtler problem. When you ask a model to rate its own confidence on a 0-to-1 scale, it gravitates toward round numbers and familiar anchors. You will see a suspicious number of 0.85s and 0.90s. The distribution is clustered, not continuous. A well-calibrated system produces a smooth distribution where every probability level has a distinct, verifiable meaning.

Some teams try to fix this with prompt engineering: "Rate your confidence as a precise decimal between 0 and 1, considering the difficulty of the question and the ambiguity of the input." The resulting numbers look more precise. They are not more accurate. You have added decimal places to a fundamentally miscalibrated signal.

## The Calibration Curve

Imagine plotting a chart. The x-axis is the model's reported confidence, binned into deciles: 0-10%, 10-20%, and so on up to 90-100%. The y-axis is the actual accuracy of responses in each bin.

A perfectly calibrated model produces a diagonal line -- when it says 70%, it is right 70% of the time. When it says 90%, it is right 90% of the time.

A typical LLM produces a curve that sits well below the diagonal in the high-confidence region. The model says 90%; reality says 62%. The model says 80%; reality says 58%. In the low-confidence region, the curve often sits above the diagonal -- the model says 30%, but reality says 45%.

The area between the diagonal and the actual curve is the calibration gap. It is measurable. And once you measure it, you can correct for it. OraClaw exposes the calibration curve as a deterministic tool — you pass in raw confidences and outcomes, you get back the corrected mapping plus a Brier score so you can track drift.

## Calibration Is Measurable and Fixable

You do not need to fix the model. You need to fix the interpretation of its outputs.

Calibration scoring takes a model's raw confidence outputs and maps them to their true accuracy using historical data. It measures the gap, models the correction function, and applies it to every future prediction. After calibration, "90% confident" actually means the model is right 90% of the time at that level.

This is not a new idea. Weather forecasting has used calibration for decades -- the reason "70% chance of rain" actually means something useful is because forecasters rigorously calibrate their probability estimates against observed outcomes. The same discipline applied to LLM confidence transforms unreliable self-assessments into actionable decision signals.

The fix is to put the calibrator next to the agent, not inside it.

```bash
clawhub install oraclaw-calibrate
```

Measure your agent's calibration gap. Correct for it. Route decisions to humans based on *actual* accuracy, not the model's feelings about its accuracy.

Your HITL threshold deserves math it can trust.

## Try OraClaw

OraClaw is an MCP server that gives Claude deterministic credit-and-decision tools — calibrated probability, monotonic constraints, audit trails. Plug it in once and every confidence score the agent emits gets corrected against actual accuracy before the HITL router sees it. Install in Claude Code:

```
claude mcp add oraclaw -- npx @oraclaw/mcp-server
```

17 tools, MIT licensed. Repo: github.com/Whatsonyourmind/oraclaw

## Get Started

- GitHub: [github.com/Whatsonyourmind/oraclaw](https://github.com/Whatsonyourmind/oraclaw)
- More posts: [dev.to/lukastan](https://dev.to/lukastan)

---

*OraClaw is the math layer for AI agents. 21 algorithms, deterministic results. [GitHub](https://github.com/Whatsonyourmind/oraclaw) | [ClawHub](https://oraclaw.com/clawhub)*
