No-code orchestration tools have a place. They are useful for routing, integrations, notifications, and operational glue. The mistake is treating them as the engineering substrate when the work involves models, evaluation, retrieval, data contracts, and failure analysis.
Python remains difficult to displace because it is not merely a programming language in AI; it is the shared laboratory of the field. The libraries, benchmarks, model interfaces, data tools, notebooks, tracing frameworks, and deployment patterns all assume that serious work eventually touches code. A visual workflow can wrap that work, but it rarely replaces the ability to inspect it.
The governance implication is direct. A team that cannot express its AI behavior in testable code will struggle to reproduce results, isolate failures, or defend decisions. Low-code tools can accelerate peripheral automation. They should not obscure the intellectual work of AI engineering.