How should I handle exceptions thrown by the AWS SDK when calling Bedrock (such as ServiceUnavailable errors or throttling exceptions)?

To handle exceptions from the AWS SDK when calling Bedrock, start by catching specific errors and implementing structured retry logic. The AWS SDK for Bedrock throws service-specific exceptions like BedrockServiceException or BedrockThrottlingException, which you can catch in try/catch blocks. For example, a ServiceUnavailable error (HTTP 503) or throttling (HTTP 429) often indicates transient issues. Capture these exceptions, log relevant details (e.g., request ID, error code), and avoid generic error handling to prevent masking root causes. Use the SDK’s built-in properties, like retryAttempts in the client configuration, to enable basic retries. However, rely on manual retry logic for finer control, especially when default SDK retries are insufficient.

Next, implement retries with exponential backoff and jitter to mitigate throttling or temporary outages. For example, after catching a throttling exception, wait incrementally longer between retries (e.g., 1s, 2s, 4s) with random variation (jitter) to avoid overwhelming the service. Most AWS SDKs provide utilities like RetryPolicy or RetryUtils to simplify this. Configure maximum retry attempts (e.g., 3–5 retries) to balance reliability with user experience. For critical operations, consider fallback mechanisms, such as queuing requests or switching to alternate workflows, if retries fail. Avoid infinite loops—always set a retry limit and surface actionable errors to users or upstream systems when thresholds are exceeded.

Finally, use circuit breakers and monitoring to improve system resilience. Tools like AWS CloudWatch can track metrics (e.g., ThrottledRequests) and trigger alerts if error rates spike. Circuit breakers (e.g., using libraries like Resilience4j) temporarily halt requests to Bedrock after repeated failures, allowing the service to recover. For instance, if 10 consecutive calls fail, the circuit opens for 30 seconds, rejecting subsequent requests and reducing load. Pair this with structured logging (e.g., JSON logs with error codes) to simplify debugging. Additionally, validate inputs and precheck quotas (e.g., model invocation limits) to proactively avoid throttling. These steps ensure graceful degradation and prevent cascading failures in distributed systems.