Start with one task
you know how to judge.

Pick something like preparing a brief, organizing review findings or structuring information from a source. Avoid making your first experiment responsible for a consequential external action.

Write down what the result should contain, what it must not invent and when a person needs to review it. Those criteria will tell you more than a successful run indicator.

Use non-sensitive material that represents the task. Include at least one case with missing or awkward information so you can see how the process handles it.

Keep a reference result or review checklist. Without one, it is easy to confuse polished language with useful work.

Choose a supported model provider and configure the required credentials. Confirm provider billing and data handling. Connect a file or tool only when it is needed for the first version of the process.

Advanced integrations and local operations may require additional configuration. Do not start by adding every system your organization uses.

Connect the input to the model task, shape the output and add the decision or review step required for the job. Use ordinary logic for exact mappings and transformations where appropriate.

Run the process and compare the result with your criteria. Fix the important failure before adding more branches or features.

The second input is where reuse begins to matter. Check whether the same instructions and structure still produce something useful.

Try the incomplete case too. Does the process identify the gap, ask a question or stop where it should? Review what the model inferred rather than assuming it followed your intent.

Create an app when another person needs a focused way to run the process. Configure batch inputs when the same task applies across multiple items. Reuse a component when another network genuinely needs it.

Do not add complexity only because the platform permits it.