Research checked 5 October 2026. Practical recommendations below are Divel’s editorial guidance; examples are illustrative.
An AI workflow can produce a convincing message while completing the wrong task. That distinction matters when automation touches lead records, campaign content or customer communications. The useful question is whether it performed the intended work correctly.
From demos to evaluation
Anthropic’s January 2026 engineering guidance describes evaluating agents with defined tasks, grading criteria and checks of the final outcome. It also distinguishes the system’s recorded actions from what actually changed in the environment. Read the engineering guidance.
Choose a narrow first workflow
Our suggested pilot is enquiry triage: read an approved enquiry, propose a category and draft a response for a person to review. Start with drafts rather than automatic sending. This creates a useful task while keeping the first deployment easy to inspect.
- Define the permitted inputs and the information the workflow may use.
- Specify what a correct result looks like, including when to ask for help.
- Limit the actions and systems available to the workflow.
- Test realistic cases before connecting it to live customer work.
- Keep a person responsible for reviewing exceptions and corrections.
Create a small test set
Include an ordinary enquiry, missing details, an ambiguous service request, irrelevant material and a request outside the business’s scope. Use synthetic or appropriately approved examples. For each case, write the expected category, the facts the draft may use and any action it must avoid.
Check factual accuracy, completeness and whether the workflow stayed within its intended task. Also review elapsed time and the amount of human correction needed. A draft that requires extensive rewriting may save little time even if it sounds polished.
Verify the outcome
If a later version updates a CRM, check the resulting record rather than accepting the assistant’s completion message. If it schedules a task, verify that the task exists in the right place. Keep a recovery route for incorrect updates.
Expand only after the pilot earns it
Run the same cases after changes to prompts, models or connected tools. Add new cases when a failure appears in practice. Decide whether to expand access based on observed reliability and business value, with a clear owner for monitoring.
This approach makes automation a measurable operational improvement, rather than a demonstration that succeeds only under ideal conditions.
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