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Agent-native

Also known as: agent-native design, agent-first

Describes software designed from the start to treat AI agents as first-class participants, not as add-ons. Agents have their own identities, permissions, and context, rather than being bolted onto a human-first product after the fact.

When a product is agent-native, the architecture assumes that some participants in a workflow are AI agents, not just humans using AI tools. That changes what the product has to do: it needs to assign agents persistent identities, manage what context each agent can access, handle asynchronous agent actions, and surface agent outputs alongside human activity without friction.

The distinction matters because most existing software was built for human users and then retrofitted with AI features. An agent-native product can do things a retrofit cannot, like letting an agent proactively surface a decision across two conversations it has been following, or giving a coding agent its own branch of a repository to work in without manual setup.

The term surfaced prominently in 2026 as a positioning shorthand for new tools (messaging apps, IDEs, cloud infrastructure) that treat agents as participants rather than plugins. You will hear it used to describe communication tools, developer environments, and workflow systems. The practical test: if you removed all the human users, would the product still function? An agent-native product usually would.

This definition is AI-generated and refreshed weekly. It may contain inaccuracies. Use your own judgment, especially for production decisions.
Related terms
AndoAgentic workflowAgent identityAgent UXMulti-agent system