What are the best practices for error handling and compensation in automated workflows?

Robust workflow error handling implements a layered strategy: retry policies (exponential backoff with jitter, max attempts per activity type), circuit breakers (trip after N failures to prevent cascade), fallback paths (alternative services or degraded mode), and compensation logic (undo completed steps when a later step fails). The saga pattern is essential for distributed workflows: each step has a corresponding compensating action. Implementation: define compensation handlers for every state-changing activity, maintain a compensation stack, and execute in reverse order on failure. Idempotency is critical: every workflow step must be safely re-executable using idempotency keys. Dead letter queues capture permanently failed items for manual review. Alerting strategy: immediate alerts for critical path failures, aggregated alerts for high failure rates, and daily digests for warning-level issues. Testing: inject failures at every workflow step during testing (chaos engineering), verify all compensation paths, test timeout handling, and simulate partial failures. Monitoring: track success/failure rates, mean time to recovery, compensation execution frequency, and dead letter queue depth.