01
Define the trigger and allowed context
Specify which enquiry starts the workflow, which approved fields it can use, which knowledge sources are in scope and which system permissions are allowed. The workflow should have a visible owner and a clear reason for each automated step. A narrow, well-defined trigger is easier to evaluate than an assistant that is asked to make open-ended decisions with incomplete business context.
02
Keep consequential decisions reviewable
AI can help classify, summarise, route or draft a response, while eligibility, pricing, clinical advice, financial decisions and other high-impact actions remain with authorised people. Design a human review point before an irreversible action and show the information a reviewer needs to accept, revise or reject it. Logs should make it possible to understand what happened without exposing unnecessary personal data.
03
Design the fallback first
External systems, automation platforms and models can fail or return an uncertain result. Queue work safely, preserve the original source context, record the reason for an exception and give the team a manual path. Evaluation should include ordinary requests, incomplete information, ambiguous language and failure conditions. A workflow earns trust when people know what it will do when it cannot proceed.
Primary references
External documentation can change. Verify implementation details against the linked official source before deployment.