Challenges and Practical Lessons

Keep the model's responsibility narrow

Do not let extraction silently turn into decision-making. A model can propose structured facts, but the engine should decide whether those facts are sufficient and valid.

Handle ambiguity explicitly

A message such as “yes” may refer to the last question, not to every pending field. The workflow must track the active question and map the answer to the correct obligation.

Treat missing, null, and false as different values

These often represent different business states. For example, replacement_card_requested = null means unanswered, while false means the customer declined.

Keep workflow content versioned

Changes to graphs, rules, prompts, and templates should be tracked. Otherwise, it becomes difficult to reproduce why a past journey behaved a certain way.

Make actions safe to retry

Networks fail. A user may double-click. Workers may retry. Idempotency keys and action records reduce the risk of performing the same operation twice.

Preserve visibility

Logs, audit journals, state inspection, and node traces make it easier to diagnose unexpected behavior than looking only at the final chatbot reply.

Separate testing from live services

A standalone test application with scripted model and connector modes makes workflow debugging faster, safer, and less expensive.