The original title is: "Why 40% of agentic AI projects fail in production: risk controls, not models"

Short summary

An in-depth analysis of why over 40% of agentic AI projects fail in production, attributing failures to inadequate risk controls rather than model quality. The article distinguishes retrieval failures from evidence override (generation-side failures), explains how hallucinations compound across reasoning chains, and compares recovery behaviors across CrewAI and LangGraph. Particularly relevant for fintech and healthtech teams facing compliance scrutiny.

• •Gartner projects 40%+ agentic AI project cancellations by 2027 due to inadequate risk controls, not model quality
• •Evidence override (generation-side failure) occurs more often than retrieval failure in RAG systems, requiring different fixes
• •Agentic loops fail quietly by continuing without hard stopping rules, compounding early hallucinations into confident wrong answers

Generated with AI, which can make mistakes.