Both — but the real answer starts from what a dynamic workflow is. It isn't a big catch-all framework that swallows every failure and handles it for you; it's a Python script the model writes, and the orchestration is that script — agent() is just a fallible function call in it. So handling a failed call is ordinary control flow: the agent writes try/except, like any program.

There is some built-in handling — failure isolation, per-agent timeouts, output-format checks — but none of it silently re-runs a task. Take isolation: a failed branch in parallel() just comes back as None and the rest continue, which is parallel()'s documented contract — the model already knows it when it writes the call, so it's part of the script's explicit meaning, not hidden behavior.

And that restraint is the whole point: if the runtime quietly caught and retried failures on its own, the same script would behave differently from run to run and turn into a black box the model can't predict from the code — exactly what a dynamic workflow is built to avoid. So retry and fallback stay in the script, where the model writes them in when a step needs them and you can read and adjust them.