agent-native-architecture

Agent-native architecture places autonomous agents at the core of an application to drive features. It works by using atomic tools, dynamic context, and prompt-driven governance, allowing new features to emerge from agent interactions without code changes.

You design applications with autonomous agents driving features by ensuring parity between UI actions and agent capabilities. This involves building atomic primitives as tools and using natural language prompts to execute tasks within a self-modifying agent loop.

You can add new application features without code changes by treating new functionality as natural language prompts. Agents execute these prompts using existing atomic tools, allowing the system to improve and evolve dynamically through retained context.

The best way to achieve parity between UI actions and agent capabilities is by mapping UI actions to atomic tools and prompts. This ensures any action a user can perform in the interface can also be enacted autonomously by the agent.

To start an agent-native workflow, prompt the agent to interrogate your idea and generate canonical documentation like PRD.md and IMPLEMENTATION_PLAN.md. The agent then executes tasks in a continuous loop until your project objective is achieved.

Yes, agent-native architecture explicitly supports self-modifying workflows. By retaining context and evolving prompts over time, the agent loop can dynamically adjust its behavior and improve system capabilities autonomously.