Agent-native evaluation is evaluation infrastructure designed for agents to consume, act on, and improve from, not just dashboards for humans to inspect. In a human-first workflow, eval results are something an engineer reads. In an agent-native workflow, eval results are available through APIs, traces, datasets, scorers, and policies that another agent can query and use.
For developers, the architectural shift is important. Eval results need stable schemas, low-latency access, explanations, trace links, and permissions. An agent should be able to ask, "Which test cases regressed after this prompt change?", inspect the failing traces, propose a fix, rerun the eval, and surface the result for approval.