A production Claude client needs to distinguish transient failures from requests that will fail again for the same reason, which is a reliability boundary Anthropic CCA-F<\/a> candidates should recognize. The API uses standard status classes: rate limits can return 429, transient service problems can return 5xx responses, and overload is represented by 529. By contrast, malformed requests, authentication failures, and permission errors need correction rather than repetition. That distinction belongs in the reliability layer of Claude Engineering<\/a>, not in ad hoc catch-all loops.<\/p>\n The official SDKs already retry transient connection failures, rate limits, and 5xx errors with exponential backoff twice by default, honoring `retry-after` when it is present. Application code should understand that behavior before adding another retry wrapper; stacked retry layers can multiply traffic exactly when the service is under pressure.<\/p>\n A 429 response can include a `retry-after` header telling the client how many seconds to wait. Retrying earlier is counterproductive and may extend the period in which the application remains rate limited. Where the server supplies a retry interval, treat it as the floor. If no interval is available for a retryable error, use exponential backoff with jitter so a large fleet does not reconnect in lockstep.<\/p>\n Jitter is not decorative. Without it, hundreds of workers that fail at the same moment wake at the same moment, creating a thundering herd. Randomizing the next attempt spreads recovery traffic over time and gives upstream capacity a chance to stabilize.<\/p>\n Backoff policy should be coordinated with the application’s own queue. A worker that sleeps inside the request handler consumes resources differently from one that re-enqueues work for a later attempt. Interactive products usually have a short user-facing latency budget and should fail or degrade gracefully after limited retries. Offline batch processing can wait much longer, but it should release scarce worker capacity while waiting. Define retry behavior per workload rather than sharing one global helper across chat, background ingestion, evaluation jobs, and scheduled generation. The same HTTP status can require a different product response depending on whether a person is waiting on screen or a batch has hours to finish.<\/p>\n The Messages API can be limited by requests per minute, input tokens per minute, and output tokens per minute. A workload can therefore hit a token limit even when request count looks modest. Large prompts, large tool definitions, long documents, and high output limits change the effective pressure on the service.<\/p>\n Capture the rate-limit response headers and compare them with local queue depth and token estimates. If a batch workload repeatedly consumes its entire token budget, increasing retry aggressiveness will not solve the problem; shaping concurrency, caching repeated prefixes, or spreading work across time will.<\/p>\n Every retry policy needs a maximum attempt count or time budget. Infinite retries turn a transient incident into stuck work and can hide systemic defects. When the budget is exhausted, return an error that preserves the final status, the number of attempts, the elapsed delay, and the original operation context needed for diagnosis.<\/p>\n Resilient automation<\/a> should make failure modes explicit rather than swallowing them, so callers can distinguish rejection, timeout, rate limiting, and partial streaming failure. That distinction determines whether retry, backoff, alternate routing, or manual investigation is appropriate.<\/p>\n Capacity incidents also expose why circuit breakers can be useful. If a high percentage of requests are returning overload or server errors, continuing to send every queued call through its full retry schedule can amplify the incident. A circuit breaker can temporarily reduce concurrency, reject low-priority work, or route to a degraded experience until success rates recover. That control should be driven by measured service health and open for a bounded period; it is not an excuse to hide failures indefinitely. Combined with jittered backoff, it turns retries from isolated client behavior into a coordinated load-management strategy.<\/p>\n With server-sent events, an error can occur after the HTTP connection was established and a 200 response was already returned. At that point, ordinary HTTP retry logic is insufficient because the client may already have emitted partial content to a user or downstream system. Retrying the whole request can duplicate visible text or repeat side effects triggered by partial output.<\/p>\n Design streaming consumers to distinguish connection setup failures from mid-stream errors. Buffer when exact-once display matters, or record how much output was committed so the product can recover deliberately. If the stream drives tools or external actions, never assume that restarting generation is semantically safe.<\/p>\n Every API response includes a unique request ID, and error bodies also expose a request identifier. Store it with your own trace or job ID. When a customer reports a failure, engineers should be able to move from the product request to each Claude attempt without searching by timestamp and guessing.<\/p>\nHonor server timing before applying your own backoff<\/h3>\n
Rate limits are multidimensional<\/h3>\n
Use bounded retries and preserve the original failure<\/h3>\n
Streaming errors need a separate path<\/h3>\n
Request IDs turn retry logs into usable incident evidence<\/h3>\n