agent-native-architecture

Agent-native architecture models actions as atomic tools and outcomes as prompts. It handles dynamic context by injecting it into loops, enabling flexible automation and emergent behavior across shared workspaces without requiring hardcoded workflows.

To build an agent-native application, provision atomic tools like read_file and bash, define a system prompt encoding behavior, and trigger the agent to loop until an explicit completion signal is reached, ensuring full CRUD coverage for data.

Achieve parity between UI actions and agent capabilities by modeling all user interface actions as atomic tools. This ensures seamless user-agent collaboration and allows agents to perform the same operations available in the UI across mobile and desktop contexts.

Yes, agent-native architecture supports open-ended feature prompts without coding by using granular atomic primitives. These primitives are composable, allowing the agent to handle unanticipated workflows dynamically through prompt-driven evolution.

Avoid prompt-driven architecture if your system lacks full CRUD coverage or cannot provide explicit completion signaling. It requires atomic primitives and dynamic context injection to function, so applications with rigid, non-looping workflows may not be suitable.