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ce-agent-native-architecture

Orchestrate agent-native workflows through prompt loops and atomic tools.

Updated Sep 25, 2024
Search Tags:#architecture#context#tooling#growth#prompts#parity#agent-native
One-click install
npx skills add https://github.com/kwam1na/athena --skill ce-agent-native-architecture-kwam1na
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ce-agent-native-architecture
Source: https://github.com/kwam1na/athena/tree/main/.agents/skills/ce-agent-native-architecture
Command: npx skills add https://github.com/kwam1na/athena --skill ce-agent-native-architecture-kwam1na

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ce-agent-native-architecture solves the challenge of enabling fully agent-native apps by providing a framework where agents are first-class citizens, orchestrating workflows through loops of prompts and atomic tools.

Core Features & Use Cases

  • Parity: every UI action has a corresponding agent tool
  • Granularity and composability: primitives that can be composed into new features via prompts
  • Context injection and memory: dynamic prompts that reflect current user data and state
  • Emergent capability and improvement: agent can handle open-ended requests and improve over time
  • Safe self-modification with guardrails and approvals

Quick Start

Describe a simple agent-native workflow you want to enable, and the system will orchestrate it using atomic tools and a prompt-defined loop.

Frequently Asked Questions about ce-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 use atomic tools?▼

Action parity means every UI action has a corresponding agent tool. This ensures agents can fully operate the application, allowing primitives to be composed into new features via prompts and enabling autonomous workflows.

How do I design autonomous agents that handle open-ended requests?▼

Safe self-modification is enforced through prompts and guardrails with approvals. This governance approach ensures agents can adapt and handle open-ended requests while remaining within explicitly defined safety boundaries.

How do I build an agent-native workflow with prompt loops and composability?▼

Granularity and composability allow primitives to be composed into new features via prompts. This means agents can handle open-ended requests by combining atomic tools dynamically, enabling emergent capability and continuous improvement.

Can I use MCP tooling for autonomous agents in a self-modifying system?▼

Yes, this architecture is applicable to designing autonomous agents, MCP tooling, and self-modifying systems. It uses explicit completion signaling and guardrails to ensure safe governance during agent-driven interactions.

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