autonomous-loops

You orchestrate autonomous Claude Code workflows by applying modular loop patterns that enable self-running multi-agent pipelines, reducing manual orchestration. These patterns support scalable workflows ranging from simple sequential pipelines to complex RFC-driven DAG orchestrations.

RFC-driven DAG orchestration is an advanced loop pattern for coordinating multi-agent workflows using directed acyclic graphs. It enables self-managing AI pipelines across development and production tasks by structuring autonomous loops around request-for-comments design cycles.

Dual-agent coordination with context persistence is set up by selecting modular loop patterns that maintain shared context and quality gates across iterations. This enables autonomous Claude Code loops to manage iterative development workflows without losing intermediate state.

Yes, autonomous loops support long-running AI experiments and CI-like iterative tasks by enabling self-managing pipelines. They provide risk controls and quality gates that allow dual-agent coordination to operate safely across extended development and production cycles.

The best way to start building self-managing AI pipelines is to run the skill to explore loop pattern options and begin with a simple Sequential Pipeline. This establishes baseline autonomous orchestration before advancing to complex RFC-driven DAG workflows.

You should avoid autonomous loops when tasks require tight manual oversight or lack clear quality gates, as the self-running workflows rely on risk controls and context persistence to manage iterations independently without continuous human intervention.