How do you handle exceptions and error recovery in RPA bots?
Exception handling is critical for RPA reliability because UI-based automation is inherently fragile. Exception types: (1) System exceptions: application crashes, network timeouts, element not found, unexpected popups. (2) Business exceptions: data validation failures, missing required fields, cases that don't fit the standard process. (3) Environmental exceptions: screen resolution changes, application updates that modify UI elements. Error handling strategy: implement try-catch blocks around every interaction group, classify errors as retryable (network timeout, element loading delay) or non-retryable (invalid data, business rule violation), implement retry logic with configurable attempts and delays (typically 3 retries with exponential backoff), and gracefully handle unrecoverable errors (log details, screenshot, notify, mark case for manual processing). Recovery patterns: (1) Application recovery: detect if the application is in an unexpected state, close and reopen if necessary, navigate back to a known starting point, and resume processing. (2) Queue-based recovery: work items in a queue are individually trackable, failed items are marked with error details and retried or queued for manual processing, and successful items are not reprocessed. (3) Checkpoint/restart: save processing state at key milestones so bots can resume from the last checkpoint after failure, preventing duplicate processing. Logging: log every action with timestamp, application, element, data (sanitized), and result. Include screenshots on failure for debugging. Error notification: immediate alerts for critical failures (bot stopped, security events), aggregated alerts for recurring errors (send summary every hour), and daily reports on overall bot health. Testing: test happy path, known exception paths, and edge cases (empty fields, special characters, maximum-length inputs, concurrent users).