Your first AI workflow should have clear boundaries.

Choose the task before the agent

A useful starting point is a repetitive task with clear inputs and a person who can explain what a good result looks like. Examples include preparing a service-request summary, extracting fields from an incoming document or suggesting how to route a case. These are candidate workflows, not promises of accuracy or savings.

Write down the current process before changing it. How long does a typical case take? What proportion needs an exception? Which errors are costly? Sometimes a straightforward rule or integration will solve the problem with less complexity than an AI agent.

Make permissions part of the design

An AI workflow should receive only the information and actions required for its task. Reading a record and modifying a record are separate capabilities. A system integration should enforce that boundary rather than relying only on instructions to a model.

For an initial rollout, it can be useful to let the AI prepare a recommendation while a person approves the consequential action. Decide which fields it may access, how actions are logged and what happens when a request is outside scope. Treat retrieved text as data, not as authority to change the workflow's rules.

Evaluate more than the happy path

Build a representative evaluation set before extending access. Include ambiguous requests, incomplete records, conflicting documents and unavailable dependencies. The important question is whether the workflow reaches an acceptable business outcome, including knowing when to stop or ask for help.

A workflow may generate a correct answer but fail to save the result. It may retry an action that already succeeded. Tests should cover those integration failures, with protections against duplicate actions and clear recovery behavior.

• Completion: was the intended business task actually completed?
• Quality: was the result correct enough for the agreed use?
• Intervention: how often did a person need to step in?
• Economics: what was the total cost per completed task?

Keep improving after release

Start with an agreed, limited scope. Compare the new process with the baseline, review representative outcomes and keep a route back to the existing process. Expansion should follow evidence rather than the quality of the demonstration.

Business rules, models and integrations can change. Evaluations and monitoring therefore belong in ongoing maintenance. The AI workflow becomes another supported part of the system, with an owner and an improvement backlog.