What is Question Flow for?

Question Flow is used when you need your AI agent to collect structured information from a user through a guided, multi-step conversation.

It is best for situations where the AI agent needs to ask questions one by one, validate the answers, store each response in a separate field, and make the collected information available for the next steps in the flow.

Question Flow is especially useful for intake forms, qualification flows, onboarding, bookings, applications, requests, and any process where the final output needs to be clean, typed, and operationally useful.

What Question Flow does

Question Flow helps your AI agent collect user information in a controlled and reliable way.

Instead of storing everything as one unstructured conversation, Question Flow saves each answer into its own field. This makes the collected information easier to review, use, and pass into later nodes in the flow.

Question Flow can:

Ask questions one by one in a set order.
Save each user answer before moving to the next question.
Validate answers as they come in.
Use AI to verify whether an answer meets your custom criteria.
Extract and normalize clean values from messy or unstructured replies.
Let users correct a specific answer without restarting the entire flow.
Show a confirmation summary before the information is finalized.
Make all collected answers available as context for AI nodes that come after the Question Flow.

When to use Question Flow

Use Question Flow when your AI agent needs to collect specific information from a user and the final data needs to be structured.

Common use cases include:

Lead intake
Customer qualification
User onboarding
Appointment or booking requests
Support intake
Application forms
Order or service requests
Any process that requires accurate user-provided details

For example, if your AI agent needs to collect a user’s name, email address, company size, preferred date, and request details, Question Flow can ask for each item separately and save each answer in its own field.

Why Question Flow is useful

Normal conversations can be flexible, but they can also be messy. Users may answer multiple questions at once, skip details, use unclear wording, or correct themselves later.

Question Flow is designed to handle that more reliably.

It helps the AI agent guide the user through the process while still supporting natural conversation. If a user gives an incomplete answer, provides something invalid, or asks to change a previous response, Question Flow can handle that without restarting the whole flow.

How Question Flow works

Question Flow works by asking configured questions one at a time.

The AI agent asks the first question.
The user replies.
The answer is saved to the field connected to that question.
If validation or AI verification is enabled, the answer is checked.
If the answer passes, the AI agent moves to the next question.
If the answer does not pass, the AI agent can ask the user to correct it.
This continues until all questions are completed.
The user is shown a confirmation summary, if confirmation is enabled.
The collected answers become available to the next nodes in the flow.

Extract and normalize

The Extract and normalize option helps turn a user’s natural reply into a clean stored value.

This is useful when users provide more information than needed or answer in a casual way.

For example:

User reply: “Sure, my email is alex at company dot com”
Stored value: alex@company.com

Or:

User reply: “My name is Alex Johnson, but you can call me Alex”
Stored value: Alex Johnson

When enabled, AI extracts the relevant value and stores it in a structured format.

Use this when you want cleaner data for fields such as names, email addresses, phone numbers, dates, company names, locations, or other structured values.

AI Verification

AI Verification allows the AI agent to check whether a user’s answer meets the criteria you define.

When enabled on a question, the AI reviews the answer using your instructions. If the answer does not meet the criteria, the AI can ask the user to provide a better or more complete answer.

For example, you can use AI Verification to check whether:

A company description is specific enough.
A request matches the services you offer.
A booking request includes enough detail.
A qualification answer meets your requirements.
A user’s response follows your custom rules.

This is helpful when basic validation is not enough and the answer needs to be judged based on meaning or context.

Confirmation step

The confirmation step gives the user a chance to review the collected information before the flow continues.

The AI agent can show a summary of the answers and ask the user to confirm whether everything is correct.

If the user notices a mistake, they can point out the specific field that needs to change. The AI can then correct only that field instead of restarting the entire Question Flow.

For example, the user might say:

“Actually, the email is wrong. It should be alex@newcompany.com.”

The AI can update only the email field and keep the rest of the collected answers unchanged.

Using collected answers later in the flow

All answers collected in Question Flow are available as context for AI nodes that come after it.

This means later nodes can use the collected information to personalize responses, make decisions, generate summaries, route the conversation, or continue the workflow using the structured data.

For example, after Question Flow collects a user’s request details, an AI Answer node can use those details to generate a tailored response or next-step message.

Question Flow vs. a regular AI conversation

A regular AI conversation is useful for open-ended interaction.

Question Flow is better when you need reliable, structured data.

Use Question Flow when:

You need specific fields filled out.
You want answers saved separately.
You need validation before moving forward.
You want the user to confirm the final information.
The collected data will be used by later nodes or operational processes.

Best practices

Keep questions clear

Ask one thing at a time. Clear questions make it easier for users to answer correctly.

Use validation where accuracy matters

If a field is important, add validation or AI Verification so the flow can catch incomplete or incorrect answers before continuing.

Use extract and normalize for messy inputs

Enable extraction and normalization when users may answer in a natural or unstructured way.

Add a confirmation step for important flows

Use confirmation when the collected information will be used for bookings, requests, lead qualification, or any workflow where accuracy matters.

Avoid making the flow too long

Only ask for the information you actually need. Shorter flows are easier for users to complete.