How to Fact-Check Your AI Agent's Answers Using Authoritative Data Sources

Your AI agent just told a user that Brazil's GDP growth was 4.2% last year. Is that right? How would you even check? This is the hallucination problem — and it's not going away. LLMs generate plausible-sounding answers, but they don't actually know facts. They pattern-match from training data that might be outdated, biased, or just plain wrong. The Real Cost of Wrong Answers A McKinsey survey found that 65% of organizations using generative AI reported at least one accuracy incident in production. In finance, healthcare, and policy — wrong numbers aren't just embarrassing, they're dangerous. The fix isn't better prompting. It's grounding your AI in authoritative data sources. What Makes a Data Source "Authoritative"? Not all data is created equal. Here's the hierarchy: Level Source Type Example Trust Score 🏛️ Government National statistics offices US Census Bureau, China NBS ⭐⭐⭐⭐⭐ 🌐 International UN/World Bank/IMF World Bank Open Data ⭐⭐⭐⭐⭐ 🔬 Research Universities, think tanks Our W