AI actions bring the power of large language models directly into your HoopAI workflows. Instead of relying solely on static templates and fixed logic, you can insert an AI-powered step that reads contact data, generates intelligent responses, and feeds its output into every subsequent action in your workflow.Whether you need to draft a personalized follow-up email, score a lead based on conversation history, or generate dynamic content for an SMS, AI actions make it possible without writing a single line of code.
AI agent configuration panel showing custom action setup for workflow integration
AI actions consume AI credits from your HoopAI plan. Monitor your usage in Settings > AI credits to avoid unexpected overages.
At a high level, an AI action is a workflow step that sends a prompt to an AI model and stores the response as a custom value you can reference later. The flow looks like this:
A trigger fires (form submission, tag applied, appointment booked, etc.)
The workflow reaches the AI action step
HoopAI assembles the prompt using your template and any merge fields
The AI model processes the prompt and returns a response
The response is stored and available to all downstream steps
This means you can use AI output in emails, SMS messages, internal notifications, if/else branches, webhooks, and more.
Adding an AI action step inside the workflow builder
1
Open the workflow editor
Navigate to Automation > Workflows and either create a new workflow or open an existing one. Add a trigger if you haven’t already.
2
Add the AI action step
Click the + button to add a new action. Search for AI in the action list and select GPT-Powered AI Action. Drag it to the desired position in your workflow.
3
Configure the model
In the action settings panel, choose the AI model you want to use. HoopAI supports multiple model options with different speed and quality trade-offs. For most business use cases, the default model provides the best balance.
Selecting the AI model for your workflow action
4
Write your prompt
Enter the instructions the AI should follow. Be specific about the desired output format, tone, and length. Use merge fields (covered below) to inject contact-specific data.
Choosing the action type for the GPT-powered workflow step
Writing a custom prompt with merge fields for the AI action
5
Set the response variable
Give the AI response a custom variable name such as ai_response or lead_score. This variable becomes available to every action that follows in the workflow.
6
Test and activate
Use the Test button to preview the AI output with sample contact data. Once you are satisfied with the results, activate the workflow.
Custom action details panel showing endpoint URL and configuration options
Triggering a workflow from an AI action showing the workflow selection interface
You are a friendly sales assistant for a home services company.The following lead just submitted a form:- Name: {{contact.first_name}} {{contact.last_name}}- Service requested: {{contact.custom_field.service_type}}- Budget: {{contact.custom_field.budget_range}}- Notes: {{contact.notes}}Write a short, personalized follow-up email (under 150 words) that:1. Thanks them by first name2. Acknowledges the specific service they need3. Suggests a next step (booking a call or site visit)Use a warm, professional tone. Do not use placeholder text.
Testing a custom action to verify AI output before activating the workflow
The more context you provide in your prompt, the better the AI output will be. Include relevant custom field data, conversation history, and specific formatting instructions. For advanced prompting strategies, see prompt engineering best practices.
Naming the AI action for use in subsequent workflow steps
Use the AI output to branch your workflow logic. For example, if the AI returns a numeric lead score, you can route high-scoring leads to your sales team and low-scoring leads to a nurture sequence.
Pass the AI-generated content to external systems via webhook payloads. This is useful for updating CRMs, triggering third-party automations, or logging AI decisions.
Store the AI output directly on the contact record by mapping the response variable to a custom field. This creates a permanent record of the AI’s analysis.
Goal: Send a personalized follow-up email within minutes of a form submission.Setup:
Trigger: Form submitted
Step 1: Wait 5 minutes
Step 2: AI action — prompt the AI to draft a follow-up email based on the form responses stored in custom fields
Step 3: Send email using the AI response as the body
Why it works: Every lead receives a message that feels hand-written and addresses their specific needs, even at 2 AM on a Sunday.
AI lead scoring
Goal: Automatically score incoming leads so your sales team focuses on the best opportunities.Setup:
Trigger: Contact created
Step 1: AI action — provide the AI with the contact’s source, form responses, and any available conversation history; instruct it to return a score from 1 to 10 with a brief justification
Step 2: Update contact — store the score in a custom field
Step 3: If/else — if score is 7 or above, notify sales team; otherwise, add to nurture sequence
Why it works: The AI considers multiple data points simultaneously and provides consistent, explainable scoring that would take a human minutes per lead.
Content personalization
Goal: Customize marketing messages based on the contact’s industry, role, and past interactions.Setup:
Trigger: Tag applied (e.g., “interested_in_demo”)
Step 1: AI action — prompt the AI to generate a demo invitation message tailored to the contact’s industry and company size
Step 2: Send SMS or email with the AI-generated content
Why it works: Instead of one-size-fits-all templates, each contact receives messaging that speaks directly to their context. A restaurant owner gets different messaging than a dental practice.
Intelligent appointment prep
Goal: Give your sales team a pre-call brief generated by AI before each appointment.Setup:
Trigger: Appointment booked
Step 1: AI action — compile all known data about the contact (source, conversations, form responses, tags) and ask the AI to generate a brief summary with suggested talking points
Step 2: Internal notification — send the AI brief to the assigned team member
Why it works: Your team walks into every call prepared, with relevant context and suggested approaches based on everything HoopAI knows about the contact.
Tell the AI exactly what format you need. If you want a numeric score, say “Return only a number from 1 to 10.” If you want an email, specify “Write a complete email with subject line and body.”
Workflow outputs often feed into SMS (160 characters) or email snippets. Always specify maximum length: “Keep your response under 100 words” or “Write exactly 2 sentences.”
Tell the AI who it is: “You are a professional customer service representative for a plumbing company.” This ensures consistent tone across all automated messages.
Consider what happens when merge fields are empty. Add instructions like: “If the service type is not provided, ask about their general needs instead.”
When available, separate your system instructions (role, rules, constraints) from the user-level content (contact data, specific request). This produces more reliable outputs.
Adjusting the temperature setting to control AI response creativity
Configuring where the AI action output is stored for use in downstream steps
Always review AI-generated content before enabling a workflow for production use. Test with diverse contact data to ensure the AI handles edge cases gracefully. Inappropriate or inaccurate AI responses can damage customer relationships.
