Building a workflow migration engine: Harness/Pi agents or LangChain/LangGraph?

I'm building an AI-powered migration engine that converts ETL workflows from platforms like Alteryx, Azure Synapse , and eventually other tools into Databricks (PySpark/SDP) . I'm evaluating two different architectures: Using Harness AI agents / Pi agents to orchestrate the migration workflow. Building the orchestration myself using LangChain + LangGraph . The engine will need to: Parse workflows into an intermediate representation (IR). Handle nested workflows/macros. Perform tool mapping (e.g., Alteryx → PySpark). Generate production-ready code. Support multi-step reasoning, validation, and