Comment by icyfox

Yep, that’s exactly it. imo the main value add for agents:

- Each LLM call and tool call becomes a checkpoint. Without durable execution you're pretty much stuck doing db saves/restores if you detect that this agent might have a previous context window. Agent frameworks are fundamentally just harnesses around the underlying model: you `while loop` until some server gives you some payload back, you add that to your context window list, and you keep going until it terminates. Durable execution moves that loop into the workflow body itself (a quick example here https://www.waymark.sh/python/tutorials/building-a-from-scra...).

- Long waits. Sometimes you do want your agent to wait for an arbitrary amount of time (ie. wait for this compilation job to finish - look again in 5mins, or maybe 5 hours). Durable execution makes sure that sleep timer will wake up regardless of what happens in the system state.

- Easier monitorability. Instead of having to sift through interleaved logs to figure out what was going on, you can see every large function call with a clean input/output contract. Useful especially in tracing down raised Exceptions and the data flow that it took to get there. Good for agents and regular code alike.

Of course, if your system is largely idempotent or your agents are mostly classifiers that are working in short internals - a simple db save/restore can work perfectly well too.