ML Agent Reported Job Canceled While GPU Cluster Kept Running

An ML operations agent case study highlights a critical gap between agent-level cancellation and actual job termination on an external GPU scheduler. When an operator instructed the agent to cancel a fine-tuning run, the agent halted its own orchestration and reported success — but the job request had already been handed off to the scheduler. The scheduler subsequently accepted the job, which began consuming reserved compute despite the operator believing no allocation was active. The agent also risked losing the job ID needed to locate and properly cancel the workload. Experts recommend agents treat such cancellations as pending until the external scheduler confirms the job's final state, and actively retrieve the job ID to request and verify termination.