workflow-orchestrator

Multi-agent AI workflows are orchestrated by defining configurable patterns that coordinate agents in sequence or parallel, managing task dependencies and feedback loops to automate complex software development processes end-to-end.

Ralph loops are self-correcting AI workflow cycles that feed execution failures back into the input stream, enabling iterative task refinement until defined success criteria and stop conditions are fully met.

Yes, multi-agent coordination supports both sequential and parallel execution within a single workflow configuration, allowing you to manage dependencies across agent roles like planner, builder, and verifier dynamically.

To set up a build and verify workflow, select a predefined pattern like Build+Verify or Multi-Agent, then configure agent roles, tools, stop conditions, and success criteria to automate iterative feature implementation.

Automating iterative AI coding tasks is best handled using feedback-driven execution patterns that loop builder and verifier agents together, feeding quality check failures back into the input for continuous code refinement.

No external dependencies are required to configure multi-agent AI orchestration patterns; the system uses internal scripts and references to define agent roles, tools, and feedback loops for workflow execution.