autonomous-loops

You orchestrate autonomous AI agent workflows by chaining non-interactive agent calls into sequential pipelines, supporting everything from simple daily task automation to complex multi-agent DAG systems. This enables persistent context management and parallel agent coordination.

Agentic loops are architectural patterns for running autonomous AI agents continuously without human intervention. You need them for large-scale feature generation and complex workflows requiring persistent context, error recovery, and automated quality gates across iterations.

Set up a sequential development pipeline by chaining non-interactive agent calls to automate daily development tasks on your feature branch. The pipeline executes tasks in sequence, managing context persistently across each automated step.

Yes, autonomous agent orchestration supports parallel agent coordination through advanced patterns like RFC-driven DAG systems. This allows multiple agents to work concurrently while maintaining persistent context and automated quality verification.

Automated quality gates in autonomous agent loops work by implementing cleanup passes and verification steps after each iteration. This ensures code quality is maintained across autonomous cycles without requiring constant human review or intervention.

Simple sequential pipelines chain non-interactive agent calls for daily task automation, while RFC-driven DAG systems coordinate multiple agents in complex directed acyclic graphs for large-scale feature generation with parallel execution and advanced error recovery.