autonomous-loops

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.

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.

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.

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.

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.

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.