autonomous-loops

You can orchestrate AI agents for autonomous workflows by implementing sequential pipelines, REPLs, or multi-agent DAG architectures that support parallel execution, context persistence, and automated quality gates.

Automating a development loop with LLMs involves defining a sequential pipeline where agents first implement a feature, then clean up the code, and finally verify the build automatically without manual intervention.

Yes, you can run multiple AI agents in parallel for content generation by utilizing multi-agent DAG architectures that support parallel agent execution alongside context persistence and quality gates.

Yes, autonomous agent orchestration supports quality gates and context persistence, allowing you to maintain state across complex multi-agent DAG systems and verify that automated development workflows meet standards.

No, you do not need external dependencies to build autonomous AI agent loops, as the skill provides self-contained patterns and architectures for automated development workflows and multi-agent orchestration.

You should use a multi-agent DAG over a sequential pipeline when your automated workflows require parallel agent execution and complex routing, whereas simple sequential pipelines suit linear tasks like implementing and verifying builds.