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Decompose features into self-contained phases by identifying clear inputs, outputs, and acceptance criteria for each phase. Organize phases as a directed acyclic graph (DAG) to model hard and soft dependencies, enabling parallel execution where possible. This reduces risk and allows teams to work on independent phases simultaneously.

Workflow orchestration automates the execution sequence of interdependent phases in a feature rollout. By defining phases as a DAG with explicit dependencies, you optimize which phases run in parallel and which must complete first. This accelerates delivery while maintaining code stability across complex features.

Yes. Generate a YAML manifest that declares each phase's inputs, outputs, dependencies, and acceptance criteria. This manifest serves as a structured, machine-readable plan for orchestration systems, enabling automated validation and execution of your phased feature rollout.

Define explicit acceptance criteria and outputs for every phase. Each phase must yield a complete, deployable increment with clear boundaries. This self-contained approach reduces integration risk and makes it safe to merge or run phases independently or in parallel.

Hard dependencies block a downstream phase until its prerequisite completes; soft dependencies allow a phase to proceed with caution or fallback behavior if needed. Distinguishing them in your DAG helps teams prioritize work and identify safe parallelization opportunities.

Decompose features when they affect multiple systems, risk breaking changes, or require coordination across teams. Phasing reduces the blast radius of each deployment, enables parallel work, and makes it easier to roll back or iterate on individual components without restarting the entire effort.