AI Agents: Why the Gap Between Demo and Deployment Keeps Widening

AI agent s are impressive, but 40%+ of enterprise projects fail. The math is brutal: a 10-step task with 95% per-step accuracy yields only 60% end-to-end reliability. Case studies (Devin, Replit, AWS Kiro, Klarna) reveal a pattern, autonomous agents fail unpredictably in production. The industry is converging on constrained architectures: MCP adoption, human-in-the-loop patterns, and domain-specific agents with limited action sets. The future isn't autonomous AI, it's reliable AI with clear boundaries. Read All.