Most systems enforce rate limits to stay healthy. Hitting a limit isn’t a failure — it’s a signal to slow down. Good automations treat this as a first-class concern.
In enterprise settings, reliability is not “it works on my laptop.” It’s predictable behavior under load: bounded retries, controlled concurrency, and clear reporting so operators can see what happened without guessing.
What “good” looks like
- Use exponential backoff with jitter when you get 429/503 responses.
- Batch work and cap concurrency (avoid “thundering herd” patterns).
- Make operations idempotent where possible (safe to retry).
- Record progress so retries don’t duplicate side effects.
Backoff in plain language
Exponential backoff means waiting longer after each failure (e.g., 1s → 2s → 4s → 8s). Jitter means adding randomness so many clients don’t retry at the same moment. Together, they prevent synchronized retry spikes.
Idempotency: the secret to safe retries
- If a request might be retried, design it so repeating it doesn’t create duplicates.
- Use stable keys (hash of input, URL, identifier) and store results by key.
- Separate “compute” from “side effects” (send email, charge card, publish).
- Backoff + jitter implemented.
- A max retry time is enforced (avoid infinite loops).
- Partial failures are recorded for later review.
- Duplicates are prevented with stable keys (e.g., dedupe by URL or identifier).
A tool that fails silently under load creates hidden costs: rework, mistrust, and operational drag. Clear limits, transparent progress, and conservative retries make automation safe to depend on.