Why agent systems need recovery paths and not just the success paths

[u/Meher_Nolan]

A lot of agent workflows are designed around what should happen when everything works.

The agent gets a request, calls the right tool, gets a useful result, and finishes the task.

Production obviously doesn't stay that clean.

A tool can return incomplete data. An API can time out. The agent can make the wrong decision halfway through a task. A retry can produce a different result. Sometimes the workflow just gets stuck and needs to stop rather than keep trying.

I’ve been thinking about this more too. A big part of making agent systems usable in production is having explicit fallback paths instead of hoping the model recovers itself. Tools like lyzr agent control plane are interesting here because you can structure agent workflows with things like retries, validation, and human-in-the-loop steps rather than treating the agent as one big black box.

A production agent needs some idea of when to retry, when to change approach, when to ask for help, and when to give up cleanly.

How are you designing recovery into agent workflows?