What Makes an API Tool AI-Native?

A discussion post asks what capabilities should define an "AI-native" API development tool, listing multi-model endpoint testing, OpenAPI schema support, automated API testing and validation, mocking for AI/inference services, connected documentation, and cross-team collaboration as expected features. The post cites Apidog as a tool combining API design, testing, documentation, mocking, and collaboration in one workflow, and asks developers building with Hugging Face Inference APIs or other LLM services what they consider essential in an AI-native Postman alternative.

As AI applications increasingly depend on inference APIs, I’m wondering what should actually make an API development tool “AI-native.” For me, it goes beyond sending requests. I’d expect: Easy testing across multiple model endpoints OpenAPI support for evolving schemas Automated API testing and validation Mocking for AI/inference services Documentation that stays connected to the API Collaboration across developers and ML teams I’ve been looking at Apidog because it brings API design, testing, documentation, mocking, and collaboration into one workflow. For people building with Hugging Face Inference APIs or other LLM services, what would you consider essential in an AI-native Postman alternative?