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agent-native-architecture

Extract agent-native architecture Skills from SKILL.md frontmatter and body.

1|Updated Apr 13, 2026
Search Tags:#architecture#context#orchestrator#mobility#parity#prompt-native#agent-native
One-click install
npx skills add https://github.com/weorbitant/compound-engineering-feat-python-plugin --skill agent-native-architecture-weorbitant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-native-architecture
Source: https://github.com/weorbitant/compound-engineering-feat-python-plugin/tree/main/skills/agent-native-architecture
Command: npx skills add https://github.com/weorbitant/compound-engineering-feat-python-plugin --skill agent-native-architecture-weorbitant

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Build applications where software agents are first-class citizens, enabling autonomous workflows, MCP tooling, self-modifying behavior, and features that emerge from agent loops rather than static code.

Core Features & Use Cases

  • Parity: ensure every UI action can be achieved by an agent using primitives or prompts.
  • Granularity: rely on atomic primitives so features emerge from prompts rather than hard-coded workflows.
  • Composability: new capabilities arise by prompts, not new code.
  • Emergent Capability: agents can solve open-ended problems and adapt to user needs.
  • Improvement Over Time: accumulate context and prompt refinement to improve behavior over time.

Quick Start

Outline a minimal agent-native loop and map core UI actions to primitives to validate parity.

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 traditional application design?▼

Agent-native architecture treats software agents as first-class citizens, enabling autonomous workflows, self-modifying behavior, and features that emerge from prompt composability and agent loops rather than static, hard-coded code.

How do I ensure UI and agent parity when building autonomous workflows?▼

To achieve UI and agent parity, map every core UI action to atomic primitives so agents can execute the same operations via prompts, ensuring capabilities are fully accessible through both manual and autonomous interactions.

Can I compose new agent capabilities without writing new code?▼

Yes, agent-native architecture enables new capabilities to arise from prompts rather than new code, allowing agents to compose atomic primitives dynamically to solve open-ended problems and adapt to user needs.

How do I validate a minimal agent-native architecture loop?▼

To validate a minimal agent-native loop, map core UI actions to atomic primitives to verify parity, ensuring that features emerge from prompt orchestration and agent loops rather than static workflows.

Does an agent-native approach support MCP tooling and self-modifying behavior?▼

Yes, agent-native architecture explicitly supports MCP tooling and self-modifying behavior by accumulating context and refining prompts over time, enabling agents to autonomously adapt and improve their workflows.

What are the limitations of relying on emergent capability from agent loops?▼

Relying on emergent capability requires strict parity mapping and atomic primitives; without these, agent loops may produce unpredictable results, making safety considerations and prompt-injection scanning essential for review.

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