Engineering Agentic Systems Beyond the Demo

Agentic AI systems often fail in production because demos rarely account for unreliable APIs, partial completion, workflow crashes, human approval needs or runaway costs. The article explains five engineering patterns for dependable agents: retry strategies, partial failure recovery, human-in-the-loop checkpoints, durable state persistence and cost bounding. It frames agent reliability as a software architecture problem rather than a model intelligence problem. Technology leaders should assess agentic systems by failure handling, operational controls and recoverability before treating them as production-ready.