AI API rate limits: build a document workflow that fails safely

A batch of invoices should not become a batch of duplicate records when an AI service slows down. Here is a practical design for keeping document work traceable through rate limits and uncertain responses.

Start with a reviewable output

A small business uploads supplier documents in batches. In this hypothetical design, one PDF becomes a draft record containing a supplier name, invoice reference, currency, total and a link to the original. A person checks it before anything enters the accounts system. These are illustrative requirements, not a customer deployment or a claim of extraction accuracy.

The useful completion state is “ready for review”. A successful API response alone is insufficient: the expected fields must exist, validation must pass, and the draft must reach the agreed destination. A document with an unreadable total should become an exception, not a confidently completed record.

Put a controlled queue before the AI step

Assign each accepted document a job identifier and store its processing state. Put a reference to the authorised document in a durable queue, then let workers pull jobs at a controlled rate. Keep a visible “received, waiting, processing, ready or needs attention” status so an employee does not upload the same file again simply because processing is slow.

A queue buffers a burst; it does not increase an API allowance. Limit the workers’ combined request rate against the provider’s applicable quotas. If new jobs consistently arrive faster than they finish, the backlog will keep growing. Microsoft’s queue-based load-leveling guidance explains this trade-off. Decide how much waiting the business can tolerate before accepting more work or switching to its manual process.

Give retries a budget and a deadline

Separate a temporary overload from a request that needs correction. Consult the provider’s error codes, rather than retrying every unsuccessful response. AWS’s SDK retry documentation distinguishes throttling, transient failures and non-retryable errors, and describes exponential backoff with jitter: increasing waits with random variation to spread retry traffic.

For this document workflow, set a maximum number of attempts and an overall job deadline. Give each network call a timeout inside that deadline. Allow time for waiting and saving the result; three individually acceptable calls can still exceed the employee’s useful waiting window. Choose values from the actual provider, document sizes and measured behaviour, not a universal number copied from an example.

Inspect the SDK’s existing retries before adding workflow-level retries. Nested policies can multiply calls and delay. Microsoft’s retry pattern recommends coordinating that responsibility.

Protect the draft record from duplicates

A timeout leaves an awkward question: did the remote action fail, or did its successful response disappear? AWS’s idempotent API guidance explains how stable request identifiers help make repeated attempts safe. Use the same operation identity for retries of the same intended write; a deliberate new processing run needs an explicit new version.

For our example, “create review draft for job 482, version 1” is one operation. A duplicate delivery should find that draft rather than create a second row. Enforce uniqueness durably at the write boundary. A separate “check then insert” can race when two workers run together. Where the destination lacks suitable duplicate protection, reconcile uncertain writes before replaying them. Amazon SQS standard queues can deliver a message again; queue delivery alone is not an end-to-end exactly-once guarantee.

Stop with an owner, not an endless loop

An expired credential needs an access fix. A corrupt PDF needs a replacement. Missing invoice fields need review. None becomes healthy through unlimited retries. When an allowed attempt or time budget is exhausted, preserve the job’s status and route it to a named operator with the next action.

A dead-letter queue is one option for isolating work that repeatedly fails; Amazon SQS documents this approach. It still needs monitoring, retention and a replay decision. For the illustrative invoice, the operator could request a clearer file while the remaining documents continue. Replaying should follow a diagnosed fix and a duplicate check.

Test what happens when the answer never arrives

Record the job and operation identifiers, attempt count, elapsed time, error category, next eligible attempt, final state and resulting draft reference. Keep raw document contents and secrets out of routine logs. Make queue age and unresolved exceptions visible to the person responsible for the workflow.

Test a throttled call, a lost response after a successful write, duplicate delivery, an invalid document and a worker restart. The acceptance question is concrete: can an operator locate each job, explain its state and recover it without creating another draft?

Explore CompileSquad’s workflow services or discuss one document process, subject to access and feasibility checks. Follow CompileSquad on LinkedIn for practical AI and infrastructure field notes.