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Builder.io’s Agent-Native is an open-source TypeScript framework for building AI agents alongside a purpose-built application UI. Its defining idea is to define an application capability once with defineAction(), then let both the agent and the UI invoke that same action. The project also describes shared application data and context, so an agent can work with the same records and relevant screen state that a person uses.
What is Agent-Native?
Agent-Native is Builder.io’s framework for applications in which an AI agent is part of the product rather than a separate chatbot bolted onto it. The project describes an agent and a human-facing interface working through shared application capabilities, data, and context.
That distinction is about architecture, not a guarantee that an agent will always behave correctly. The framework’s documentation explains its intended design and capabilities; it is not an independent assessment of reliability, security, or production readiness.
How does Agent-Native work?
Define an action once
The core pattern is defineAction(). A developer defines an operation in TypeScript, including its input validation and implementation. The agent can call that operation as a tool, while the UI can call it from application code. The project’s README says both paths use the same validation, permissions, and implementation.
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For example, an application might define an action for updating a project record. Rather than separately implementing a button handler and an agent-only tool, the UI and agent can invoke the shared action. The exact behavior still depends on how the application defines permissions, inputs, and the action itself.
Use an action layer beyond the UI and agent
The project documentation describes its action layer as serving the UI and agent as well as HTTP, MCP, A2A, and CLI surfaces. Its example action uses a Zod input schema, an HTTP method, and a run function. This lets application capabilities be exposed through more than one interface without requiring the agent to simulate a person operating the screen.
As the project README puts it: “The agent does not click through the UI. It works through the same action layer as the UI.”
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How do an AI agent and a web app share actions and state?
Shared actions
The shared action is the bridge between a human-facing control and an agent tool. A person might initiate an operation in the UI; an agent might call that operation in response to a request. Both use the action definition rather than maintaining separate implementations for the same capability.
Shared data and application context
Agent-Native describes agent work as visible in the UI and UI work as available to the agent. It also describes passing relevant application context to the agent—for example, the current page, selected record, or active view. The official site says users and agents read and update the same PostgreSQL data source of truth.
In practice, this design is intended to let a person inspect, edit, approve, or share work in the interface while the agent acts on application capabilities and context. What an agent can see or change depends on the application’s implementation and permissions; shared state does not itself establish that every action is appropriate or error-free.
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Can an AI agent use the same actions as a React UI?
Yes, that is the central documented pattern. A React UI can call an action from code, and the agent can use the action as a tool. The project also documents HTTP and integration surfaces. This is not the same as having an agent control a browser by clicking buttons: the agent works through the application’s action layer.
What capabilities does the project document?
Agent-Native’s repository and official site list these built-in areas:
- Agent chat, including an embedded-chat option described by the site.
- Authentication, organizations, and permissions for resources.
- Skills and memory.
- Automations and agent teams.
- PostgreSQL for production and PGlite for local development.
These are capabilities described by the project, not a feature-by-feature independent audit. The project gallery also lists example applications for Clips (meeting, screen, and voice-note capture), Design, Slides, Analytics, Calendar, Mail, Assets, Content, and Plans. They illustrate the kinds of applications the framework presents; their presence in a gallery is not evidence of third-party certification or commercial adoption.
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What do you need to build with Agent-Native?
The project is written in TypeScript and is MIT-licensed. Its documentation says developers choose their model, database, and host, and keep application code in their own repository. It documents PostgreSQL for production and PGlite for local development on a Nitro-compatible host. The framework does not make model or infrastructure costs disappear: its site says developers bring their own model, database, and infrastructure.
The repository’s documented quick start creates a standalone project using the chat template:
npx --yes @agent-native/core@latest create my-agent --standalone --template chatThat command uses the latest package tag, so the version resolved can change over time. Check the project’s current installation documentation if you need a pinned version or different template.
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What Agent-Native does—and does not—establish
Agent-Native offers a specific architectural approach: share capability definitions and application context between an agent and the UI. That can avoid maintaining a separate implementation for every interface, but the framework’s published descriptions do not establish that deployments are bug-free, secure by default, fast, or production-ready for every use case.
The official materials reviewed do not provide a deployment benchmark, security certification, or independent production-readiness assessment. There is also no substantiated basis here for ranking Agent-Native against other agent frameworks. Teams evaluating it should assess how its action model, permissions, state access, integrations, and operational requirements fit their application.
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