Miscalibrated Confidence in Enterprise Pharma AI

Interactive technical document exploring how to build reliable pharmaceutical AI agents that accurately assess their own confidence.

Live: ignatpenshin.github.io/pharma-agent

Problem

Enterprise pharma AI agents suffer from miscalibrated confidence — claiming high certainty on errors and low certainty on correct answers. This document presents a system architecture that makes failures predictable and recoverable rather than attempting to eliminate them.

What's Inside

1. Taxonomy of False Confidence — 10 failure mechanisms across retrieval, reasoning, and epistemic blindness categories
2. System Architecture — Layered RAG + Meta-Cognitive Classifier with 6 pre-generation signals
3. 5-Zone Response System — GREEN/YELLOW/ORANGE/RED/GRAY confidence zones with distinct response behaviors and human-in-the-loop routing
4. Calibration Pipeline — Two-stage confidence model (logistic regression → isotonic regression) with cold-start protocol
5. Eval-for-Eval — How to evaluate the evaluator: golden datasets, per-dimension reliability, inter-annotator agreement
6. Hard Questions — Honest gaps that engineering alone cannot close

30+ peer-reviewed references. All links verified.

Features

• Interactive footnotes explaining technical terms (calibration, RAG, NLI, GRADE, AUROC, etc.)
• Scroll-tracking navigation rail
• Context sidebar with key metrics and zone distribution
• Responsive layout (desktop → tablet → phone)
• Color-coded confidence zones throughout

Tech

React 19 · Vite · Vanilla CSS · GitHub Pages

Run Locally

npm install
npm run dev

Deploy

npm run deploy

Author

Ignat Penshin — AI Engineer