agent-native-architecture

Agent-native architecture is a design approach where AI agents serve as first-class core components rather than hardcoded functions. You need it when building autonomous, outcome-driven applications that require maximum flexibility and emergent capability from LLMs.

Designing composable tools for autonomous AI agents involves building primitive, granular functions that agents can combine dynamically. This approach ensures agent loops manage execution effectively, allowing systems to self-modify and achieve complex goals through emergent capability.

The best way to test LLM agent loops is implementing strategies that validate agent outcomes rather than just code coverage. This ensures your autonomous systems reliably achieve their defined goals and maintain performance during refactoring or migration.

Yes, you can migrate an existing codebase to an agent-native application by refactoring features to be defined by agent goals. This process applies principles like parity and composability to transition toward self-modifying, autonomous software.

Core principles of building prompt-native autonomous systems include parity, granularity, composability, emergent capability, and improvement over time. These concepts guide how AI agents interact with tools and execute loops within the software.