Start with the limits that apply to your job
Batch automation is useful when the same AI task must run across many items: summarizing documents, classifying support messages, or drafting descriptions from a catalog. The simplest script sends one request per item as quickly as possible. That can work for a small test, but a larger run needs a plan for rate limits. An API may limit requests, input and output tokens, concurrent work, or some combination of these. Limits can also vary by model or account, so check the provider’s documentation and your account settings rather than assuming a universal safe speed.
Before building the batch, estimate its shape. Count the items, note which ones contain unusually long inputs, and decide how much output each task needs. A hundred short labels and a hundred long summaries may have similar request counts but very different token demands. Run a small sample first to observe typical usage and errors. Treat that sample as a planning aid, not a guarantee: later items may be longer or harder than the ones you tested.
Make the work queue small enough to control
Put each item in a queue with a stable identifier, its input location, and a status such as pending, in progress, complete, or failed. Workers can then take items from the queue at a controlled pace. This separates the size of the overall batch from the number of requests in flight. If the API or network slows down, the queue waits; it does not create an uncontrolled burst of requests.
Choose work units that are easy to retry. For example, processing one document per job may be simpler than placing an entire folder into one request. Combining several short items can reduce overhead, but it also makes failures and output-to-item matching more complicated. Whatever unit you choose, keep a record of the prompt or task version used. If you change instructions halfway through a run, you should be able to tell which outputs came from which version.
Pace requests using both concurrency and token budgets
Set a maximum number of simultaneous requests, then add a rate controller that decides when another request may start. Concurrency alone is not a rate limit: fast requests can still create too many starts in a short period. A controller might track a rolling budget or issue permission to start at a steady pace. Leave headroom beneath any published limit, especially when other tools share the same account.
If token usage is constrained, reserve an estimated amount before starting each job. Include the input, reusable instructions, and a reasonable allowance for the response. Reconcile the estimate with reported usage when the request finishes, if that information is available. Estimates will not be perfect, so long inputs and generous output settings deserve extra room. When a provider offers an asynchronous batch mechanism, compare its rules with your needs; it may suit work that does not require immediate results, but it still needs monitoring and error handling.
Retry thoughtfully instead of retrying immediately
A rate-limit response is a signal to slow down, not to launch the same request again at once. If the response provides a retry delay, respect it. Otherwise, use increasing waits between attempts, with a small random variation so multiple workers do not retry together. Apply a retry cap and record the final failure for review. Temporary network or server errors may also merit retries; malformed inputs and authorization errors usually need a fix rather than another attempt.
Design for uncertain outcomes. A connection can fail after a request was accepted but before your script receives the result. Use an API-supported idempotency feature when one is available and appropriate. In your own queue, keep each item’s identifier and check for an existing saved result before writing a new one. For actions with side effects, such as sending a message or changing a record, consider a separate review or confirmation step. A retry should not silently perform the action twice.
Save progress and inspect the finished batch
Persist results as jobs finish rather than holding everything in memory until the end. Save the item identifier, status, output, usage details if available, and a concise error description. This lets you restart after an interruption and retry only unfinished or failed items. A simple dashboard or log showing completed jobs, pending jobs, retry counts, and recent errors can reveal a pacing problem before the entire batch stalls.
Finally, check the work itself. Rate-limit compliance does not mean the outputs are correct or usable. Validate required fields, flag empty or oddly formatted responses, and review a sample for task quality. Be careful with sensitive inputs: send only the data the task requires and protect stored prompts and outputs according to your project’s needs. A dependable batch is one that stays within its operational limits, recovers from interruptions, and leaves a clear trail of what it produced.
