From AI demos to reliable workflows
A practical way to turn an impressive AI demonstration into a bounded workflow: define its job, test its failure paths, and decide what counts as finished.
The field note
A demonstration shows that a task can succeed under the conditions you observed. An operating workflow needs a more complete answer: which inputs it accepts, what it is allowed to change, how completion is checked, and who handles an exception. Writing those rules early makes the first implementation easier to evaluate and the first failure easier to explain.
Anthropic’s guide to building agents recommends starting with simple approaches and distinguishing predefined workflows from agents that choose their own steps. Our practical interpretation is to assign autonomy deliberately. If a sequence can be specified and checked in ordinary code, keep it there. Use a model for the uncertain interpretation inside that sequence, rather than making every step an open-ended decision.
