agent-native-architecture

Agent-native architecture is a design paradigm where AI agents are first-class citizens, enabling autonomous operation. It shifts focus from rigid workflows to emergent capabilities by treating features as outcomes achieved by agents using atomic, primitive tools.

Design tools for LLM agents by focusing on atomic, primitive tool design patterns that empower agents rather than restrict them. This approach, combined with composability principles, allows agents to dynamically combine tools to achieve emergent capabilities.

The core principles for building agent-native applications include parity, granularity, composability, emergent capability, and improvement over time. These principles define the framework for system prompt design, context injection, testing, and architectural strategies.

Yes, you can refactor an existing application to leverage agent-native architecture. The framework provides guidance on refactoring existing code to increase flexibility and achieve emergent functionality by adopting dynamic context injection and tool design patterns.

Manage context for autonomous AI agents by implementing dynamic context injection and file-based workspaces. This approach ensures agents have the necessary environmental context to operate autonomously while maintaining system prompt design integrity.

The best way to test agent-native applications is to apply the framework's testing and refactoring guidelines, which evaluate whether atomic tools and composability patterns produce the desired emergent capabilities and improvement over time.