agent-native-architecture

Name: agent-native-architecture
Availability: InStock
Author: Ladi

Move agent decision logic into prompts and primitive tools.

1|Updated Oct 30, 2025

Search Tags:#architecture #agents #tools #parity #self-modification #prompt-native #shared-workspace

Ready to run

npx skills add https://github.com/iamladi/cautious-computing-machine--primitives-plugin --skill agent-native-architecture-iamladi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-native-architecture
Source: https://github.com/iamladi/cautious-computing-machine--primitives-plugin/tree/main/skills/agent-native-architecture
Command: npx skills add https://github.com/iamladi/cautious-computing-machine--primitives-plugin --skill agent-native-architecture-iamladi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompt-native architecture solves the problem of brittle, hard-coded AI behavior by moving decision logic into prompts and primitive tools, enabling agents to reason through outcomes rather than execute fixed workflows.

Core Features & Use Cases

Feature Prompts: define outcomes and decision criteria in prompts, not in code.
Primitives over Workflows: use simple primitives (read, write, list, call API) to enable any workflow.
Context-Driven Design: inject dynamic app context (resources, activity, capabilities) into prompts for accurate behavior.
Use Case: build autonomous agents, MCP servers, or self-modifying systems that adapt over time.

Quick Start

Define your first prompt-native agent by outlining its goals, selecting primitives, and coding a minimal system prompt to guide behavior.

Frequently Asked Questions about agent-native-architecture

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

FAQPage Schema

What is prompt-native agent architecture and how does it differ from hard-coded workflows?▼

Prompt-native agent architecture moves decision logic into prompts and primitive tools instead of hard-coding behavior, enabling AI agents to reason through outcomes and adapt dynamically rather than execute fixed workflows.

How do I build autonomous agents using dynamic context and primitive tools?▼

To build autonomous agents, define desired outcomes in prompts, select simple primitives like read, write, and list, and inject dynamic app context such as resources and capabilities to drive adaptive behavior.

When should I use prompt-native design for an MCP-style server?▼

Use prompt-native design for MCP-style servers when you need agents to handle dynamic contexts and self-modifying systems safely, utilizing primitives and CRUD-like governance rather than rigid, pre-programmed workflows.

Does prompt-native architecture support safe self-modification for AI agents?▼

Yes, prompt-native architecture supports safe self-modification by applying specific controls and CRUD-like governance, allowing autonomous agents to adapt over time while maintaining safe execution boundaries.

What are the limitations of using prompts and primitives for agent decision logic?▼

Relying on prompts and primitives shifts complexity from code to context management, requiring careful injection of dynamic app context and robust shared workspaces to prevent unpredictable agent behavior and ensure action parity.

Can I use shared workspaces to coordinate actions across multiple prompt-native agents?▼

Yes, shared workspaces coordinate actions across prompt-native agents by providing a common environment for reading, writing, and managing dynamic context, ensuring synchronized behavior without hard-coded dependencies.