Agent-Native Architecture: Software Agents Can Operate

Agent-native architecture enables AI agents to operate software end to end by making every user-accessible capability programmatically discoverable and callable, returning machine-parseable typed results, and allowing agents to read back state created in earlier steps. It differs from AI-enabled interfaces and conventional API-wrapped products because a REST API alone may lack runtime action discovery, stable structured output, matching read paths for writes, or agent-friendly error handling. The approach recommends streaming typed NDJSON events with a terminal result and meaningful exit codes so agents can monitor progress and distinguish failed assertions from environmental errors. Human consent should generally be collected at a review stage rather than before every routine action, while permissions and scoped credentials should limit high-risk operations. Organizations can retrofit existing products incrementally by adding structured output, exit-code contracts, callable alternatives to dashboard-only actions, state readback, and finally an MCP-based discovery layer. Maintaining agent-native behavior requires automated contract tests that verify action discovery, schemas, exit-code semantics, access boundaries, and successful execution in real environments.

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