Seven Laravel Patterns to Build Reliable, Auditable AI Agents in Production

A technical guide published on DEV Community outlines seven design patterns for building production-grade AI agents using the Laravel PHP framework. The patterns address common failure points such as silent retries, untracked state mutations, and budget overruns that make AI agents unpredictable in real-world deployments. Key recommendations include persisting every agent run as a database-backed record, offloading execution to queues outside the HTTP request cycle, and enforcing strict contracts and risk levels on every tool the agent can call. Additional patterns cover validating model output before acting on it, managing context with token budgets and data redaction, and requiring human approval before destructive or costly actions are taken. The guide also emphasizes observability and evaluation as prerequisites before any prompt or model changes are shipped to production.