System Design, Not AI, Is the Real Culprit in Agent Failures

As AI agents move into critical business applications, incidents blamed on 'AI failures' are revealing a deeper problem: the absence of basic systems architecture principles. From prompt injection vulnerabilities to unbound retry loops and missing output policies, these failures echo well-established distributed-systems problems from decades past. Understanding that these are engineering issues, not model issues, is crucial for building trustworthy AI systems.