AI can build most of this Sidekiq monitoring system, but a human needs to wire it into production safely.

The core pattern — instrument jobs, capture failures, push to Slack — is well-trodden and structurally repeatable. However, 40+ jobs with different retry policies, business criticality, and failure semantics mean each integration point requires contextual judgment about what 'anomalous' actually means per job.

The high-level goal is clear (visibility, alerting, dead-letter management), but success criteria like alert thresholds, what counts as 'stuck,' and acceptable retry counts are undefined and will vary by job type. An agent can produce a working system but cannot self-validate that the thresholds are correct for this business.

The agent needs access to the actual Rails codebase, existing Sidekiq configuration, Redis setup, Slack workspace credentials, and deployment pipeline — none of which are provided in the task description. Without codebase access, the agent produces generic scaffolding that requires significant human adaptation.

Incorrectly instrumented Sidekiq middleware can silently swallow jobs, cause double-processing, or crash workers handling invoice generation and email campaigns — all high-stakes, partially irreversible operations. A bad deployment here could corrupt financial records or spam customers.

Choosing alert severity levels, deciding which jobs warrant PagerDuty escalation vs. Slack noise, and tuning anomaly detection thresholds requires operational intuition about the business. The code itself is automatable; the policy decisions embedded in it are not.