================================================================================ ARTICLE: How to Build a 'Red-Team' Agent That Tests for Factual Errors URL: https://omniscient.news/blog/build-red-team-agent-factual-errors Published: 2026-04-15 Updated: 2026-04-01 Category: AI Agents & LLMs Tags: red team, AI agents, fact-checking, adversarial testing, quality control ================================================================================ A red-team agent adversarially checks an article's claims before publication, explicitly trying to find errors that standard fact-checkers miss. A red-team agent is designed to fail your article — its objective is to find every error, inconsistency, misleading claim, and unverified assertion in a draft before it's published. Unlike a standard fact-checker (which attempts to verify claims), a red-team agent attempts to falsify them, using a different set of sources and reasoning strategies than the original fact-checking process. Red Team Agent Design Objective: "Your goal is to identify every factual claim in this article that might be wrong, misleading, or missing important context. You are not trying to agree with the article — you are trying to find errors." Methodology: For each specific claim, the agent searches for counter-evidence (sources that contradict the claim), alternative interpretations, and missing context that would change the meaning of the claim. Output: A list of potential problems with severity ratings (critical, moderate, minor), supporting evidence for each concern, and recommended editorial actions. Human review: A human editor reviews all critical and moderate concerns before publication. Minor concerns are addressed at their discretion. Frequently Asked Questions Q: undefined A: undefined Q: undefined A: undefined Q: undefined A: undefined