AGI-Bench: The Truth Layer for AI Agents

Automatically verify if AI agents lie about the data they report, reducing trust crisis

Problem

Developers report that when LLM agents process real data, they frequently fabricate numbers and cite incorrect sources, making outputs untrustworthy. Posts show tools designed to catch AI agents lying about data, while others focus on decision logging and cost transparency.

Solution

Provides a lightweight API and CLI to automatically compare key facts in AI outputs against source data. Agents call the service during report generation to validate each claim, receiving confidence labels and original citations. Teams integrate it into existing agent workflows to intercept unreliable outputs.

Users

Developers and data teams using LLM agents for data processing who need high reliability

MVP

Support passing facts and data sources to output validation results and confidence; provide REST API and simple Python/JS SDK; middleware for popular agent frameworks like LangChain; web dashboard to view historical validations

Revenue

Usage-based API billing, or subscription tier including more validation credits and private deployment

Why now

As AI agents handle critical business data, demand for accuracy surges, yet general models lack built-in reliable fact-checking. Indie developers can quickly offer a lightweight, focused validation layer.