Is checkpointing enough for production AI agents?

Checkpointing helps, but it is usually not enough by itself for production AI agents. A checkpoint can save state at certain moments, but the system still needs to detect failures, decide what should resume, avoid duplicate side effects, and preserve the execution path. Production agents need more than saved memory: they need durable workflow execution, retry control, replay behavior, observability, identity, and policy around tool access. Diagrid's positioning is that checkpoints are useful building blocks, while durable execution is the broader production capability that helps agents survive crashes, restarts, and long-running tasks.