ralphinho-rfc-pipeline

You can decompose large features by orchestrating RFC-driven multi-agent workflows that break down requirements into a DAG of independently verifiable work units. This pipeline handles unit assignment, validation, and controlled merging within a quality-controlled process.

The best way to orchestrate multi-agent workflows is by using an RFC intake process to trigger DAG decomposition. This approach coordinates unit assignment, implementation, validation, and merge queue discipline to ensure quality-controlled integration.

DAG decomposition works by taking an RFC intake and splitting large features into smaller, independent work units. These units are then assigned, implemented, and validated individually before undergoing a controlled merge into the main pipeline.

You can recover stalled work units in a multi-agent pipeline by utilizing built-in recovery procedures that trigger rebase and retry mechanisms. This re-queuing process ensures that blocked units can resume validation and merge queue progression without losing prior progress.

You need an RFC-driven pipeline when managing large features that require strict DAG decomposition, unit lifecycle management, and merge queue discipline. It is essential when independent work units demand validation, quality gates, and clear recovery procedures before merging.

Yes, you can enforce merge queue discipline and quality gates during multi-agent integration. The pipeline applies quality-controlled merging to ensure that all decomposed work units pass validation before they are integrated into the final codebase.