One agent running one task is a solved problem. The trouble starts when you have fifty of them, or five hundred, all reaching for the same model at the same time. LLM rate limit concurrency is the part of agent architecture that never shows up in the demo and always shows up in production, usually as a wave of failed requests the first time real load arrives.
The good news is that none of this is new. Every high-throughput system that depends on a shared, metered resource has faced it, and the patterns that work for databases and payment gateways work here too. They just have to be applied deliberately, because an agent fleet generates load in bursts that are easy to underestimate until they knock.
Two different ceilings
When you scale agents, you are pushing against two limits at once, and it helps to keep them separate.
The first is the provider's rate limit. Model APIs cap how many requests and how many tokens you can send per minute. Cross the line and you get throttled, typically with a "too many requests" response, regardless of how much you are willing to pay. This ceiling is outside your control; you can request more, but on any given day it is a fixed wall.
The second is your own resource ceiling. Each in-flight agent holds memory, a connection, maybe a worker process running tests or git operations. Launch unlimited agents and you exhaust your own machines long before you help anyone. This ceiling is yours to manage, and managing it well is most of the job.
Confusing the two is a common mistake. Adding machines does nothing if the wall you are hitting is the provider's rate limit, and requesting a higher provider limit does nothing if your own workers are saturated. Measure which one you are actually hitting before you spend money on either.
Cap concurrency on purpose
The first and most important control is a deliberate cap on how many agent requests are in flight at once. Not "as many as arrive." A number you chose.
An uncapped system under load does the worst possible thing: it launches everything, overwhelms both ceilings, and every request degrades together. A capped system launches up to its limit, queues the rest, and keeps the launched work healthy. Throughput is higher with the cap than without it, which is counterintuitive until you have watched an uncapped fleet collapse.
Pick the cap from the tighter of your two ceilings. If the provider allows a certain request rate, your concurrency times your average request time should stay under it. If your own workers saturate first, cap there instead. The right number is empirical: start conservative, watch for throttling and resource pressure, and raise it until you approach one of the walls.
Queue the overflow, do not drop it
A cap only helps if the work beyond it waits somewhere. That is a queue. Requests arrive, the queue holds them, and workers pull the next one as capacity frees up.
A queue turns a spiky, unpredictable arrival pattern into a smooth, bounded flow the system can actually sustain. It also gives you a place to reason about fairness and priority, which matters the moment you have more than one kind of work. Latency-sensitive interactive requests can jump ahead of background analysis; a small urgent task need not wait behind a large slow one. Without a queue you have no such lever, because everything is already running.
Treat a throttle as backoff, not failure
Here is the distinction that separates a fragile fleet from a robust one: a rate-limit response is not a task failure. It is the provider saying "not right now." Treated as a failure, it aborts work that was perfectly good. Treated correctly, it triggers a wait and a retry.
The standard technique is exponential backoff with jitter. When a request is throttled, wait a short interval and try again; if it is throttled again, wait longer; and add a random jitter to each wait so that a thousand agents throttled at the same instant do not all retry at the same instant and throttle each other again. The jitter is not a nicety. Synchronized retries are how a brief throttle becomes a self-inflicted outage.
Backoff and requeue also pairs naturally with the retry discipline that keeps the rest of an agent robust. A throttle, a timeout, and a transient error all want the same response: wait, retry, and only give up after a bounded number of attempts. That machinery is worth building once and reusing, and it is the same machinery covered in idempotency and retries when your worker is an LLM, because a retry is only safe when the underlying action is safe to repeat.
Keep one tenant from starving the rest
If you run agents for more than one customer, or even more than one project, raw concurrency control is not enough. One tenant firing a thousand tasks can consume the entire cap and leave everyone else waiting, which reads to those customers as an outage you cannot explain.
The fix is fairness: give each tenant a share of the capacity rather than first-come-first-served across the whole fleet. A per-tenant concurrency limit, or a fair queue that interleaves work across tenants, keeps one heavy user from monopolizing the system. It also bounds the blast radius of a runaway: a tenant whose tasks spiral hits their own ceiling and backs off, rather than dragging down every other tenant with them.
The honest limitation
None of these patterns create capacity. They ration it. If your sustained demand genuinely exceeds what the provider will grant and your infrastructure can serve, concurrency caps and queues will keep the system healthy but work will wait, and no amount of clever backoff changes that. At that point the answer is a higher provider limit, more infrastructure, or less work, not a better scheduler. The patterns here keep you stable and fair up to your real ceiling; they do not raise the ceiling.
There is also a tuning cost. Caps set too low waste capacity you are paying for; set too high they invite the collapse they were meant to prevent. The right numbers come from watching real load, not from a formula, and they drift as your workload and the provider's limits change.
At Loopsfinity this matters because we run many customers' work at once: each customer's share is bounded so no one starves the others, and a provider throttle is treated as a reason to slow down and retry, not to fail the work. The details of how we schedule and enforce that are ours; the patterns above are not, and they are the ones any team running agents at scale will end up reaching for. They sit alongside the broader questions in AI agent architecture, and they interact directly with how you coordinate work across services in one product, many repos.