How to Evaluate a Tool That Claims to Be Agent-Native

Agent-native software is defined here as a product that allows unattended programs to perform the same consequential tasks as human users without browsers, interactive sessions, or borrowed credentials, a claim that demonstrations alone cannot verify. The article recommends testing vendors during trials by running common workflows from a clean CI container with scoped machine credentials and evaluating published machine-readable schemas, typed outputs, actionable and retryable errors, idempotent operations, headless provisioning and teardown, agent-specific permissions, and API-accessible execution traces. It also urges buyers to measure tool-definition and response token costs, document observed results rather than impressions, and prioritize headless first-run capability because GUI-dependent setup is a common failure point. Citing regulatory scrutiny of unsubstantiated AI claims, the piece argues that vendors should provide evidence for their assertions, while acknowledging that interface operability does not measure output quality, production load behavior, cost at scale, or long-term reliability. TestMu AI Agent Testing is presented as an example of a command-line-oriented platform, though the article also discloses an error-response shortcoming in its own tooling.

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