sr-batch-implement

Batch feature implementation orchestrates multiple features by validating the dependency graph and executing independent tasks in parallel waves. It coordinates dependent and independent feature refs under strict dependency constraints, ensuring a cohesive execution plan.

Dependency graph validation checks feature references to enforce execution order constraints before running waves. It identifies independent tasks suitable for parallel execution and sequences dependent tasks, preventing circular dependencies and ensuring a valid batch execution pipeline.

Yes, batch orchestration supports dry-run execution to validate the dependency graph and wave planning pipeline without applying changes. This allows you to verify parallel execution concurrency controls and task sequencing before committing to the full batch run.

The best way to manage parallel execution concurrency is through configurable concurrency limits applied during wave execution. This controls how many feature implementations run simultaneously within a wave, preventing resource exhaustion while maintaining dependency-aware batch orchestration.

Batch feature tracking provides per-wave progress dashboards during execution and generates a final batch report upon completion. These dashboards monitor parallel task statuses across all feature refs, giving real-time visibility into the wave-based orchestration pipeline.

Wave-based batch orchestration is not suitable for single feature implementations or tasks with highly dynamic, unpredictable dependencies that cannot be statically validated. If your workflow lacks clear dependency constraints or requires sequential single-task execution, avoid batch parallel orchestration.