autonomous-loops

Design autonomous AI agent loop architectures from sequential pipelines to RFC-driven DAGs.

Updated Mar 12, 2026

Search Tags:#automation #ai agents #workflows #dag #continuous development #llm orchestration

Ready to run

npx skills add https://github.com/ryasrk/AgentBrokeTheMatrix-CopilotVersion --skill autonomous-loops-ryasrk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autonomous-loops
Source: https://github.com/ryasrk/AgentBrokeTheMatrix-CopilotVersion/tree/main/.github/skills/autonomous-loops
Command: npx skills add https://github.com/ryasrk/AgentBrokeTheMatrix-CopilotVersion --skill autonomous-loops-ryasrk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide to building and deploying autonomous AI agent loops, from simple sequential tasks to complex, multi-agent Directed Acyclic Graphs (DAGs), enabling automated development and problem-solving.

Core Features & Use Cases

Loop Patterns: Explore a spectrum of loop architectures, including Sequential Pipelines, NanoClaw REPL, Infinite Agentic Loops, Continuous Claude PR Loops, and RFC-Driven DAGs.
Automation: Design workflows that run without human intervention, suitable for CI/CD pipelines, parallel content generation, and iterative project development.
Use Case: Automate the entire process of implementing a new feature by defining a specification, having agents decompose it, implement it in parallel worktrees, and merge it via an agent-driven queue, complete with automated testing and review.

Quick Start

Use the autonomous-loops skill to explore patterns for building continuous Claude PR loops.

Frequently Asked Questions about autonomous-loops

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

FAQPage Schema

How do I build autonomous AI agent loops for continuous development?▼

Autonomous AI agent loops are built using architectural patterns ranging from simple sequential pipelines to complex multi-agent DAGs, enabling continuous development workflows that run without human intervention. This Skill provides blueprints for designing these automated systems.

What is an RFC-driven DAG system for AI agent orchestration?▼

An RFC-driven DAG system is a complex multi-agent architecture where AI agents decompose specifications into tasks, implement them in parallel worktrees, and merge via agent-driven queues. It represents the most advanced loop pattern for autonomous problem-solving.

How do I set up parallel agents in an autonomous workflow?▼

Setting up parallel agents involves configuring a Directed Acyclic Graph (DAG) workflow where multiple AI agents execute decomposed tasks concurrently across separate worktrees. The architecture includes context persistence and automated quality gates to manage the parallel execution.

Can I use autonomous workflows to automate CI/CD pipelines with AI agents?▼

Yes, autonomous workflows can automate CI/CD pipelines by implementing continuous PR loops where agents iteratively develop features, run automated testing, and perform reviews. These loops operate without human intervention, suitable for iterative project development.

What are the different loop architectures for LLM orchestration?▼

LLM orchestration loop architectures include Sequential Pipelines, NanoClaw REPL, Infinite Agentic Loops, Continuous Claude PR Loops, and RFC-Driven DAGs. Each pattern offers varying levels of autonomy and complexity for AI agent workflows.

When should I use multi-agent DAGs instead of sequential pipelines?▼

Multi-agent DAGs are necessary when automating complex processes like implementing a new feature from specification through parallel execution and merging, whereas sequential pipelines suit simpler, linear continuous development tasks without parallel worktree requirements.