autonomous-loops

Autonomous AI development loops are architectures that enable AI agents to execute iterative software tasks without human intervention. You need them for continuous CI/CD-style pipelines, automating feature development from spec analysis to merging.

You can set up autonomous CI/CD pipelines by implementing loop architectures like Sequential Pipelines or RFC-Driven DAGs. These patterns facilitate automated workflows spanning implementation, testing, code review, and merging managed entirely by AI agents.

Yes, autonomous development workflows support running parallel agents. You can achieve this by utilizing complex multi-agent DAG architectures that distribute tasks across multiple AI agents for concurrent execution and continuous iterative development.

The best way to manage context persistence in multi-agent loops is to implement dedicated context persistence patterns within your loop architecture. This ensures continuous state tracking across sequential pipelines and complex DAG systems.

Quality gates in LLM orchestration act as automated checkpoints within autonomous loops to validate code and enforce standards. They are implemented within the loop architecture to ensure AI-generated outputs meet project requirements before progression.

A sequential pipeline executes AI tasks in a linear step-by-step flow, while an RFC-driven DAG orchestrates complex multi-agent systems based on design documents. DAGs enable parallel execution and intricate dependency management.