Automation Scaling Checklist: From 10 Runs to 100,000

Ten workflows per hour and ten thousand workflows at 09:00 are different systems. Record trigger type, peak arrival rate, expected duration, external rate limits, payload size, and business deadline for each sequence.

Capacity planning based on daily averages hides bursts. Use the busiest five-minute window and the slowest acceptable downstream dependency.

Admission decides what enters the system. Concurrency decides how much runs at once. A queue without concurrency limits simply moves the outage downstream.

Set limits by scarce resource: email account, CRM tenant, model provider, database, or customer. Global worker counts are too blunt when one noisy tenant can consume every slot.

A cron trigger can fire while its previous run is still active. Decide explicitly whether the next run should skip, wait, merge, or run concurrently. The default should never be accidental.

For reports and sync jobs, a singleton lock with a bounded lease is often correct. For partitioned work, lock by tenant or date range so independent partitions still move.

AI and integration costs can jump before infrastructure metrics look unusual. Store cost beside each run, then limit by workflow, customer, model, and day.

Route deterministic transformations away from an LLM. Cap agent iterations. Reject payloads above a known size or summarize them first. A cost alarm after the invoice arrives is bookkeeping, not control.

When demand exceeds capacity, the system must wait, reject, degrade, or reroute. Pick one for each workload before the incident.

Durable queues protect data, but unlimited queues hide a growing deadline breach. Monitor age, not just depth. A queue of 500 one-second jobs is healthier than ten jobs stuck for an hour.

Run burst tests with the real downstream rate limits. Kill workers, slow the database, inject 429 responses, and verify fair progress across tenants. Inspect whether retries amplify load.

Orch8 provides persisted sequence state, concurrency controls, resource pools, and retry policies. Your capacity test still needs realistic handlers and external quotas. Synthetic no-op steps prove almost nothing.