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agent-native-architecture

Design agent-native architectures with atomic primitives and explicit completion signals.

Updated Aug 23, 2026
Search Tags:#composability#prompt-engineering#parity#granularity#context-injection#agent-native
One-click install
npx skills add https://github.com/jjeremycai/claudekit --skill agent-native-architecture-jjeremycai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-native-architecture
Source: https://github.com/jjeremycai/claudekit/tree/main/plugins/compound-engineering/skills/agent-native-architecture
Command: npx skills add https://github.com/jjeremycai/claudekit --skill agent-native-architecture-jjeremycai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves how to build agent-native architectures where autonomous agents operate in loops to achieve defined outcomes.

Core Features & Use Cases

  • Parity: ensure every UI action has a corresponding agent capability.
  • Granularity: atoms/tools are primitives; features are outcomes described by prompts.
  • Composability: ship new capabilities by prompts, not code.
  • Emergent capability: support open-ended requests and discovery of new patterns.
  • Context-injection: inject runtime app state into prompts for accurate decisions.

Use cases include designing MCP tools, self-modifying systems, and building apps where features emerge from agent loops.

Quick Start

To begin, define an atomic set of primitives (read_file, write_file, list_files, bash) and craft a system prompt that describes task outcomes, then run the agent in a loop until you reach completion.

Frequently Asked Questions about agent-native-architecture

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

FAQPage Schema
How do I build agent-native apps where autonomous agents operate in loops?▼

Agent-native apps make agents central by ensuring every UI action has a corresponding agent capability. Ship new capabilities by prompts rather than code, supporting open-ended requests and discovery of new patterns through agent loops.

What is parity in agent-native architecture and why does it matter?▼

Parity ensures every UI action has a corresponding agent capability. This guarantees autonomous agents can perform identical operations to users, making agents central to the application's core functionality and interaction model.

How do I design MCP tools using composability and atomic primitives?▼

Design MCP tools by defining atomic primitives like read_file and bash, then compose new capabilities through prompts rather than code. This composability allows shipping features via prompt engineering and emergent capability discovery within agent loops.

How does context injection work for runtime app state in prompt engineering?▼

Context-injection works by injecting runtime app state directly into prompts for accurate agent decisions. This dynamic pattern ensures autonomous agents have necessary environmental awareness to execute tasks effectively within shared workspaces.

What are the limitations of using emergent capability for self-modifying systems?▼

Emergent capability in self-modifying systems relies on agent loops supporting open-ended requests and new pattern discovery. Limitations include unpredictable behaviors requiring explicit completion signals and careful granularity management.

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