Define your input sources and expected formats before building the workflow. Document the schema, required fields, and validation rules. This prevents runtime errors when data arrives in unexpected formats. Establish clear boundaries between processing stages. Each stage should accomplish one specific task and pass clean data to the next stage. A document extraction workflow might separate OCR processing from entity recognition, allowing each to scale independently. Implement retry logic and error handling at every stage. AI model calls fail due to rate limits, timeout errors, or temporary outages. Robust workflows queue failed operations for retry rather than losing data. Use conditional branching based on model confidence scores. Low-confidence outputs route to human review queues, while high-confidence results proceed automatically. This pattern reduces manual intervention while maintaining accuracy.…