Prepare an AI workflow for real users

Before launching an AI workflow, check its data, permissions, failure behaviour, costs and human handoffs.

Trace the whole journey

Follow a user input through to the final result. List every service receiving data or taking an action. Confirm production URLs, credentials and billing accounts, and remove sample records and unsupported claims from customer-facing pages.

Keep permissions narrow

Give integrations only the access needed. Keep secrets on the server and out of public pages, logs and generated text. Review who can change prompts, approve actions and configure providers. Use separate development and production environments where practical.

Review consequential actions

Drafting a message is different from sending it to a customer. Add review before purchases, account changes or destructive actions when errors would be difficult to reverse. Make the proposed action clear enough for a person to assess.

Test failure and repetition

Try missing data, a timeout, a failed provider and a duplicate event. Decide whether retries are safe and bound the number of attempts. Keep useful operational records without exposing sensitive information. Avoid loops that repeatedly spend money or repeat an external action.

Budget and support the workflow

Estimate every step, including retries and connected services. Use deterministic rules for validation and routing, reserving models for work that benefits from language understanding. Provide an error-reporting route and test the owner’s review process. Start with a scope you can observe and support before increasing autonomy.