AI agents should retrieve facts, not define them
Your unsupervised agent is making business decisions it has no authority to make. Here's why that kills trust.
Why it matters
Production agents fail not because they hallucinate, but because they're forced to resolve ambiguous data without authority. The fix: build resolution layers (semantic layers, feature stores, retrieval ranking) that let humans decide once, then let agents retrieve — not define.
The key facts
6 to knowLLM accuracy on business logic: 97% at 5 rules, 73% at 15 rules, 43% at 50 rules across 9 models
Unsupervised agents (customer service, analytics, embedded chatbots) cannot use human-in-the-loop without destroying their value
Real failure case: agent querying multiple disagreeing APIs for customer address; same question produced different answers across sessions
Solution pattern: encode business decisions (reconciliation rules, source ranking, metric definitions) into infrastructure (feature store, retrieval tool, semantic layer + MCP) before agent sees data
Deterministic, consistent wrong answer is worse than supervised human error — no accountability, no audit trail
Query generation against raw databases unreliable in production; semantic layer with constrained tool set (metric name, dimensions, time frame only) reduced token use and latency while guaranteeing consistency
Go to the source
CIOcio.com
Publisher excerpt: I have made this mistake before. The goal was to create an executive intelligence agent: an artificial intelligence (AI) engine where C-suite executives could self-serve their analytics and pull trusted numbers for the board. The promise was strong: personalized dashboards, ad hoc analyses,…