Resume the Evaluation, Not the Entire Batch: Build a Checkpoint-Aware AI Job Controller With Temporal

Large AI evaluations rarely fail at convenient boundaries. A batch may contain tens of thousands of prompts, retrieval cases, tool-use scenarios, or judge-model comparisons, and each case can involve expensive network calls plus result persistence. Restarting the entire batch after a worker crash wastes inference spend and can change the meaning of the run when model outputs are nondeterministic.