Multi-step document processing workflows, such as those involving financial documents, require precise orchestration of steps like classification, extraction, validation, and synthesis, often managed by AI agents. Observability is essential in these complex workflows to provide visibility into how unstructured content is transformed into structured data, helping to identify and resolve issues like incorrect classification and validation failures. The blog introduces LlamaIndex Agent Workflows as a tool for enhancing observability in document workflows, using open-source technologies like OpenTelemetry and Jaeger. It details setting up a financial document classification and extraction workflow, demonstrating how to integrate comprehensive observability to monitor performance and detect failures. The article also illustrates creating custom event traces to capture specific metrics, emphasizing the importance of observability in improving system reliability and user experience in AI-powered document processing.