AI can draft the dead-letter queue scaffolding, but a developer must own the wiring and testing.

The dead-letter queue pattern is well-established in Laravel and Redis, giving the agent strong structural footing. But the legacy codebase's specific job classes, exception types, queue names, and existing infrastructure introduce unique context that varies per project.

Core requirements are reasonably crisp — exponential backoff, DLQ, 7-day retention, dashboard, alerts — but key details are unspecified: alert thresholds, which monitoring system to integrate, dashboard tech stack, and what 'manually reprocess' means in the existing UI. These gaps require judgment calls.

The agent has no access to the actual codebase, Redis configuration, existing job classes, or infrastructure. Without reading the real code, it can only produce generic Laravel patterns that may conflict with the legacy setup and will require significant manual adaptation.

This touches a payment-processing queue — mistakes could silently drop payment jobs, double-process transactions, or corrupt queue state. Errors here are potentially irreversible and financially damaging, demanding careful human review before any deployment.

Architectural decisions — retry limits, backoff ceilings, alert thresholds, whether to use Laravel Horizon or a custom solution — require judgment about the business context and risk tolerance. The coding patterns themselves are learnable, but the integration decisions are not.