How to Build Safe, Production-Ready AI Agents in Laravel

A technical guide published on DEV Community outlines best practices for deploying AI agents in Laravel beyond the prototype stage. The article warns that dangerous agents are not those that give wrong answers, but those that confidently take harmful actions, loop uncontrollably, and leave no recoverable audit trail. It argues that Laravel's existing primitives — queues, validation, policies, transactions, and logging — make it well-suited for building reliable agent systems when used correctly. Key recommendations include persisting every agent run as a durable record, moving reasoning to queued jobs, enforcing approval gates for high-risk actions, and treating all model output as untrusted until validated. The guide emphasizes that a production AI agent should be treated as a supervised workflow engine, not a chatbot, with strict tool contracts, budget limits, and shadow-mode evaluation before any prompt changes go live.