Builder.io's Agent-Native is an open-source TypeScript framework for building applications where an AI agent and a purpose-built interface share the same capabilities, data and state. The project had approximately 5,400 GitHub stars at the time of review and is distributed under the MIT license.

The core idea is to define a business capability once as an action. The agent can call that action as a tool, while the React interface calls the same implementation from code. Validation, permissions and business logic therefore do not need to be recreated separately for the chat layer and the traditional application.

What the framework includes

Agent-Native exposes actions through the UI, HTTP, MCP, A2A and a command-line interface. It includes patterns for authentication, permissions, shared state, skills, memory, scheduled automation and teams of specialist agents.

For storage it supports PostgreSQL in production and PGlite in local development. Developers bring their own language model, database, tools and infrastructure, which reduces dependence on one model provider.

Source and documentation: Builder.io Agent-Native on GitHub.

Practical use cases for Karim

A marketing operations product could let an agent draft content, inspect campaign data and prepare a publishing change while the interface shows the proposed output, sources and approval status. A healthcare workflow could summarize non-clinical enquiries or route tasks, while sensitive actions remain behind explicit human approval.

The framework is also relevant for internal dashboards where people need to inspect and edit the agent's work rather than accept a black-box answer. Shared state means the agent can understand the selected campaign, patient-service category or report currently visible to the user.

Limits and risks

Agent-Native is evolving quickly and does not yet have broad independent evidence for large regulated deployments. A shared action layer improves consistency, but it also makes permission design critical. Developers still need audit logs, least-privilege access, data separation, tests and approval gates for publishing, payments or healthcare data.

Karim's strategic takeaway

The opportunity is to build agents as accountable products, not chatbots attached to existing software. A useful first pilot would be a bilingual content workflow where research, drafting, review, approval and publishing are visible in one interface and every external action is recorded.