se-agent-native-architecture

Design software architectures where agents act as first-class participants in execution loops.

Updated May 7, 2026

Search Tags:#architecture #prompts #tools #parity #context-injection #agent-native

Ready to run

npx skills add https://github.com/simonwjackson/pi-software-engineering --skill se-agent-native-architecture

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: se-agent-native-architecture
Source: https://github.com/simonwjackson/pi-software-engineering/tree/main/skills/se-agent-native-architecture
Command: npx skills add https://github.com/simonwjackson/pi-software-engineering --skill se-agent-native-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Software architecture for building applications where agents are first-class citizens, orchestrating work in AI-enabled loops using atomic tools to achieve outcomes with minimal hard-coded workflows.

Core Features & Use Cases

Parity: UI actions have corresponding agent tools, enabling seamless handoff between human and agent.
Granularity and Composability: Primitives are atomic; features are descriptions in prompts that compose into complex tasks.
Context and Shared Workspace: Dynamic context injection and a single shared data space enable continuous learning and collaboration.
Use Cases: Designing autonomous agents, MCP tools, self-modifying systems, and loop-based automation in production workloads.

Quick Start

Define atomic tools, write a guiding system prompt, and run an agent loop that iterates until the task is complete.

Frequently Asked Questions about se-agent-native-architecture

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

FAQPage Schema

What is agent-native architecture and when do I need it for my application?▼

Agent-native architecture is a software design pattern where agents act as first-class participants in execution loops. You need it when building autonomous agents, MCP tools, or self-modifying apps that require dynamic context injection and minimal hard-coded workflows.

How do I build autonomous agents that loop to outcomes instead of following fixed workflows?▼

To build autonomous agents, define atomic tools, write a guiding system prompt, and run an agent loop that iterates until the task is complete. This approach uses dynamic context injection and composability to handle open-ended tasks instead of relying on hard-coded workflows.

How does parity between UI actions and agent tools improve human-agent collaboration?▼

Parity ensures every UI action has a corresponding agent tool, enabling seamless handoff between human and agent. This architecture allows both to operate on the same atomic primitives within a shared workspace, maintaining continuous learning and collaboration.

Does agent-native architecture support edge case handling and safety checks for production workloads?▼

Agent-native architecture explicitly plans for edge cases, resume operations, and safety checks within its execution loop. It enforces tooling primitives and prompt-based behavior to ensure autonomous agents operate safely in production workloads.

What is the best way to design MCP tools for composability in agent loops?▼

The best way to design MCP tools is to keep primitives atomic and define features as descriptions in prompts that compose into complex tasks. This granularity combined with a single shared data space enables agents to dynamically inject context and handle open-ended tasks.