Your team needs a platform for API design, documentation, and collaboration. If you manually explore APIs, share collections, and generate SDKs.
You use AI coding agents to write backend code and want automated API regression testing and integration testing against your real local infrastructure before code hits CI.
You need a tool to manage how your team builds and shares APIs, and one to make sure your AI agent's code changes don't break them.


Postman's AI can generate test scripts, and its AI Engineer (beta) uses a Context Graph to produce more context-aware output than a general-purpose generator. But the difference is less about how tests are generated and more about what they run against.
Postman's tests run against cloud sandboxes or environments you configure.
Kerno generates integration tests, runs them against your actual databases and services in Docker locally, and maintains them as your code evolves.
It depends on how you develop. If you use AI coding agents (Claude Code, Cursor, Windsurf) to write backend code, Kerno adds automated API regression testing and integration testing against your real local infrastructure, inside your agent's development loop.
Postman is increasingly capable in AI-assisted workflows, including PR-triggered validation in sandboxed environments, but it doesn't spin up your actual databases and services locally to test every code change during development. If your workflow is manual API exploration, testing against configured environments, and team collaboration, Postman covers you.
Yes. Postman handles API design, documentation, and team collaboration. Kerno handles automated API testing, regression detection, and integration testing against real local infrastructure for AI-generated code changes. They operate at different points in your workflow.
Not yet. Kerno currently supports HTTP(S) endpoints. GraphQL, gRPC, and WebSocket support are on the roadmap.
When Kerno detects a regression or unintended behavioral change, it flags it with debug context and waits. You or your AI agent review the diff and either approve it (updating the baseline to reflect the intentional change) or reject it (so the agent can fix the regression). Nothing is auto-accepted.