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agent-native-architecture

Provide architectural patterns for agent-native software development with tool design and execution patterns.

Updated Feb 17, 2026
Search Tags:#architecture#design patterns#ai#llm#agent#autonomous systems
One-click install
npx skills add https://github.com/danfrdn/antigravity-config --skill agent-native-architecture-danfrdn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-native-architecture
Source: https://github.com/danfrdn/antigravity-config/tree/main/skills/agent-native-architecture
Command: npx skills add https://github.com/danfrdn/antigravity-config --skill agent-native-architecture-danfrdn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides the foundational patterns and principles for building applications where AI agents are first-class citizens, enabling autonomous operation and emergent capabilities.

Core Features & Use Cases

  • Agent-Native Design: Learn core principles like Parity, Granularity, and Composability.
  • Tool Design: Build atomic primitives and understand when to add domain-specific tools.
  • Execution Patterns: Implement robust agent loops with completion signals and partial completion.
  • Use Case: Design a product where users describe outcomes, and agents autonomously achieve them using tools and file system interactions, leading to applications that improve over time.

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 how does it differ from standard LLM integration?▼

Agent-native architecture is a software design approach where AI agents operate as first-class citizens with autonomous execution loops. It differs by prioritizing principles like Parity, Granularity, and Composability to enable emergent capabilities and systems that improve over time.

How do I design execution patterns for autonomous agent systems?▼

Design execution patterns for autonomous agent systems by implementing robust agentic loops with clear completion signals and partial completion logic. This involves creating atomic tool primitives and using context injection to let agents autonomously achieve user-described outcomes.

When should I add domain-specific tools to an AI agent?▼

Add domain-specific tools to an AI agent when atomic primitives are insufficient for complex interactions. Tool design in agent-native systems balances granular file system interactions and composability to unlock emergent capabilities for specific product use cases.

What are the core principles for building AI-native apps with agentic loops?▼

The core principles for building AI-native apps with agentic loops are Parity, Granularity, Composability, Emergent Capability, and Improvement Over Time. These guide tool design, execution patterns, and context injection to enable autonomous operation.

How do I build autonomous agent systems that improve over time?▼

Build autonomous agent systems that improve over time by applying agent-native architectural patterns like Emergent Capability and Improvement Over Time. This allows the product to autonomously achieve outcomes using tools and context injection, leading to continuous system enhancement.

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