Five patterns engineers use to make AI agents reliable in production

A software developer writing for DEV Community has outlined five tool-calling design patterns that distinguish production-ready AI agents from demo-grade ones. Standard tutorials rarely address failure scenarios such as tool timeouts, infinite loops, duplicate calls, or models generating fabricated responses after errors. Among the recommended patterns are enforcing a hard tool-call budget per turn to prevent runaway API costs and implementing deduplication logic to stop models from invoking the same tool repeatedly with identical arguments. The author notes these are not edge cases but routine conditions any deployed agent will encounter. Code examples using Anthropic's Claude API are provided to illustrate each pattern in practice.