What is prompt-native architecture?

It is a design philosophy where features are defined as desired outcomes in natural language prompts, letting the AI agent determine the best way to achieve them using primitive tools rather than hard-coded workflows.

Can an agent safely modify its own code using this skill?

Yes, the skill includes best practices for self-modification, including approval gates, auto-commits for rollbacks, health checks, and build verification before restarts.

How does this differ from traditional tool use?

Traditional tools encode specific business logic in code, whereas agent-native tools are simple primitives—like read/write file—that the agent combines dynamically based on its prompt instructions.

When should I avoid using an agent-native approach?

Avoid this approach for high-frequency operations requiring sub-millisecond latency, strictly deterministic requirements, or scenarios where API costs are a primary constraint.

Agent-Native Architecture

Name: Agent-Native Architecture
Author: SiTaggart

bySiTaggart

API Development

Design and build autonomous AI agents by defining outcomes in prompts rather than hard-coded workflows.

About

This skill empowers developers to transition from traditional rigid coding to a prompt-native philosophy where AI agents navigate primitive tools to achieve outcomes. It provides architectural frameworks for designing MCP servers, implementing self-modifying logic, and maintaining action parity between human users and autonomous agents. Ideal for building complex, flexible systems, it guides the implementation of dynamic context injection and shared workspaces, enabling agents to evolve their own behavior safely and effectively through natural language instructions.

Key Features

Primitive tool design and CRUD completeness patterns
Action parity alignment between UI and agent
Dynamic context injection for runtime awareness
Self-modification and evolution safety gates
Prompt-native feature definition and iteration
0 GitHub stars

Use Cases

Building autonomous agents with self-evolving codebases and prompts
Refactoring legacy workflow-heavy applications into agentic systems
Designing flexible MCP servers with dynamic capability discovery

About

Key Features

Primitive tool design and CRUD completeness patterns
Action parity alignment between UI and agent
Dynamic context injection for runtime awareness
Self-modification and evolution safety gates
Prompt-native feature definition and iteration
0 GitHub stars

Use Cases

Building autonomous agents with self-evolving codebases and prompts
Refactoring legacy workflow-heavy applications into agentic systems
Designing flexible MCP servers with dynamic capability discovery