agent-native-architecture

Guide designing agent-native applications with AI agents as core components.

5|1|Updated Aug 26, 2025

Search Tags:#architecture #ai #llm #agent #prompt-native #autonomous systems

Ready to run

npx skills add https://github.com/hollandkevint/thinkhaven --skill agent-native-architecture-hollandkevint

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-native-architecture
Source: https://github.com/hollandkevint/thinkhaven/tree/main/.gemini/skills/agent-native-architecture
Command: npx skills add https://github.com/hollandkevint/thinkhaven --skill agent-native-architecture-hollandkevint

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework and best practices for designing, building, and testing applications where AI agents are first-class citizens, enabling autonomous, outcome-driven software.

Core Features & Use Cases

Agent-Native Design Principles: Understand core concepts like Parity, Granularity, Composability, Emergent Capability, and Improvement Over Time.
Tool Design & Execution: Learn to build primitive, composable tools and manage agent loops effectively.
Testing & Refactoring: Implement strategies for testing agent outcomes and migrating existing codebases.
Use Case: Designing a new AI-powered productivity tool where features are defined by agent goals, not hardcoded functions, ensuring maximum flexibility and emergent capability.

Quick Start

Use the agent-native-architecture skill to design a new agent-native system from scratch.

Frequently Asked Questions about agent-native-architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is agent-native architecture and when do I need it for AI applications?▼

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.

How do I design composable tools for autonomous AI agents?▼

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.

What's the best way to test outcomes for LLM agent loops?▼

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.

Can I migrate an existing codebase to an agent-native application?▼

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.

What are the core principles of building prompt-native autonomous systems?▼

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.