How do you make a multi-step AI workflow more reliable?

None of these fixes make any individual step smarter. They work by changing the shape of the chain around a step whose per-call reliability never improves.

• Keep chains short. Reliability compounds multiplicatively, so every step you cut removes one multiplication, not one addition. A ten-step chain and a five-step chain at the same per-step reliability are not twice as different — the gap widens with each step you add, which is why collapsing three brittle steps into one well-built one usually beats optimizing any of the three individually.
• Add gates. Break the work into checked micro-tasks, and verify the result of one before the next runs. A gate does not have to be another model call — a schema check, a regex, or a simple assertion that a file now exists is often enough, and it is far cheaper than discovering the failure five steps later when the context that would explain it is gone.
• Retry and escalate. A step that fails 5% of the time fails twice in a row only 0.25% of the time (0.05 × 0.05), so a single retry turns a likely-eventually failure into a rare one. Escalation is the part teams skip: a retry needs a ceiling, and when it’s hit, the honest move is to stop and hand the step to a human rather than retry forever against a step that is not going to succeed on attempt six either.

These three compose. A short chain with gates at each step and one retry per gate is the default shape of a workflow built to actually run in production, not just to work in a demo. Durable execution is what makes the retry step cheap to implement — it is the infrastructure underneath “retry from here,” not a fourth fix on its own.