dag-convergence-monitor

To know when to stop iterating in a DAG workflow, monitor quality trends and plateau signals across iterations. Convergence assessment analyzes this progress to provide structured stopping recommendations on whether to continue, pause, or escalate the task.

Convergence monitoring for iterative agent tasks tracks quality improvement over iterations to detect stability or plateaus. It assesses whether results are converging toward a defined goal within a DAG-based workflow and signals when to stop.

Track quality trends across iterations in a DAG by analyzing progress data to identify improvement, stability, or plateau phases. This trend analysis guides the optimization of iteration cycles and budget allocation.

Yes, you can use convergence tracking to optimize iteration budget by monitoring real-time visibility into goal proximity and quality plateaus. This prevents wasted budget on iterations yielding diminishing improvements.

You should escalate instead of continuing iteration in a DAG when convergence monitoring detects a persistent quality plateau. Structured stopping recommendations indicate when adjustments or escalation are necessary because iterative improvements have stalled.