checkpoint

Track multi-step AI instruction progress by starting tasks, reporting incremental status, and marking completion or errors across task lifecycles. This provides visibility into which steps ran, which instruction paths are in use, and what remains pending.

Report task status during workflow orchestration by invoking a checkpoint script with current progress, total steps, and next actions. This records task summaries and guarded progress updates for operational visibility.

List pending tasks in a bash script instruction pipeline by running the checkpoint pending command. This queries tracked tasks that are incomplete, showing what remains to be processed in the workflow.

Manage instruction path usage independently by marking when a specific instruction path starts and completes. This can optionally be tied to a task ID, allowing separate tracking of instruction execution paths.

This task tracking approach works without external dependencies, implementing command-based state management via a standalone checkpoint script invocation suitable for bash-driven workflows.

You lose visibility into in-flight AI instruction tasks when multi-step workflows lack checkpointed progress reporting. Without tracking task starts, incremental status, and completion, it becomes hard to know what failed or what remains pending.