AI Agents Often Fail in Production Due to Infrastructure Issues, Not Reasoning

The ReAct pattern for AI agents appears simple in tutorials but reveals significant complexity when deployed. Common production failures stem from inadequate tool design, poor cost controls, and security oversights rather than poor model reasoning. For instance, setting iteration limits is crucial for cost management, as each step consumes tokens and can drain resources. Developers must also design tools to return clear, truncated results and handle untrusted outputs to prevent security risks like prompt injection. Finally, streaming intermediate steps is necessary for user transparency, as agents take time to process tasks.