AI agents excel at conversations. Workflows excel at automation. When you connect them, you get the best of both worlds: intelligent, natural conversations that trigger powerful behind-the-scenes automation.A standalone AI agent can answer questions and collect information. A standalone workflow can send emails and update records. Together, they can have a conversation with a lead, qualify them in real time, book an appointment, notify your sales team, add the contact to a nurture sequence, and send a personalized follow-up — all without human intervention.
The integration between AI agents and workflows operates in two directions:
Bot to workflow — The AI agent triggers a workflow based on something that happens during the conversation (e.g., the contact expresses interest, provides qualifying information, or requests a callback)
Workflow to bot — A workflow activates an AI agent to initiate or continue a conversation (e.g., a timed follow-up, a re-engagement message, or a post-purchase check-in)
Both directions can be chained together, creating sophisticated automation sequences that feel personal and responsive.
Conversation AI bot configuration showing the connection between AI agents and workflows
Setting up the Conversation AI workflow action step by step
Navigate to AI Agents and select the agent you want to connect to a workflow. Go to the agent’s Actions tab.
2
Add a workflow trigger action
In the actions list, find Trigger Workflow. This action tells HoopAI to start a specific workflow when the AI agent determines the right moment during a conversation.
Bot goals configuration for defining when to trigger a workflow
3
Select the target workflow
Choose which workflow to trigger from the dropdown. The workflow must already exist in your Automation > Workflows section. You can select any workflow that uses a compatible trigger type.
Selecting the target workflow to trigger from the AI agent
4
Define the trigger conditions
Specify when the AI agent should fire the workflow. Common conditions include:
The contact provides a specific piece of information (email, phone, service need)
The conversation reaches a particular intent (wants to book, ready to buy, needs support)
The contact explicitly requests something (callback, pricing, brochure)
5
Map conversation data to workflow fields
Pass data collected during the conversation into the workflow. Map extracted information like name, email, service interest, and budget to contact fields or workflow variables.
6
Test the full chain
Start a test conversation with your AI agent. Provide the information that should trigger the workflow. Verify that the workflow starts and receives the correct data.
The AI agent continues the conversation even after triggering a workflow. The workflow runs in the background while the agent keeps engaging the contact. This creates a seamless experience where the contact never knows automation is happening behind the scenes.
Selecting which messaging channel the Conversation AI action should monitor
Configuring branch paths based on the AI agent conversation outcomes
Configuring when the AI agent should trigger the workflow during a conversation
Selecting the workflow trigger from the AI agent action configuration
During the AI agent setup, you define which pieces of information the agent should extract. These map directly to HoopAI contact fields:
Bot extracts "service_needed" → Maps to custom field "Service Interest"Bot extracts "budget" → Maps to custom field "Budget Range"Bot extracts "timeline" → Maps to custom field "Project Timeline"
Once mapped, these fields are available to the triggered workflow via standard merge fields like {{contact.custom_field.service_interest}}.
Sometimes you need a workflow to process data and return results to the AI agent. This creates a round-trip: the bot collects information, a workflow processes it, and the bot delivers the result.
Workflow triggers: looks up pricing from a custom values list or external API via webhook
Pricing data returns to the conversation
AI agent presents the quote naturally in conversation
Configuration: Use a webhook action in the workflow to call your pricing API, then store the result in a contact custom field that the AI agent can reference.
Appointment availability check
Flow:
Contact wants to book an appointment
AI agent asks for preferred date and time
Workflow triggers: checks calendar availability
Available slots return to the conversation
AI agent presents options and confirms booking
Configuration: The workflow checks the assigned user’s calendar and updates a custom field with available slots. The AI agent reads this field and presents options.
Account status verification
Flow:
Existing customer asks about their account
AI agent collects identifying information
Workflow triggers: pulls account details from your system
Account information returns to the conversation
AI agent shares relevant details while protecting sensitive data
Configuration: Use conditional logic in your AI agent to determine what information is safe to share and what requires human handoff.
Configuring the action name when connecting an AI agent to a workflow
1. Website visitor starts chat └─ AI Agent: Greets visitor, asks qualifying questions2. Visitor provides budget and timeline └─ AI Agent: Triggers "Lead Scoring" workflow3. Lead Scoring workflow runs ├─ AI Action: Scores lead 1-10 based on all data ├─ Update Contact: Stores score in custom field └─ If/Else: Score >= 7? ├─ Yes: Tag "hot_lead", notify sales on Slack └─ No: Tag "nurture_lead"4. AI Agent reads the score from the contact field ├─ Hot lead: "Great news! Let me connect you with a specialist. │ When works best for a quick call?" └─ Nurture lead: "Thanks for your interest! I'll send you some resources that might help."5. Based on response: └─ Hot lead books appointment → Triggers "Appointment Booked" workflow └─ Nurture lead → Triggers "Drip Sequence" workflow
This chain handles the entire lead journey from first contact to qualified handoff, automatically.
Saving the workflow trigger configuration for your AI agent
Create a workflow with the trigger 'Conversation ended'
Set the trigger to fire when an AI agent conversation ends. You can filter by which agent or conversation channel.
2
Add a wait step
Insert a delay — typically 30 minutes to 24 hours depending on your use case. This gives the contact time before receiving follow-up.
3
Add conditional logic
Check whether the conversation resulted in a booking, a sale, or just an inquiry. Route each outcome differently.
4
Configure follow-up actions
Booked appointment: Send confirmation email with details
Expressed interest but did not book: Send a “still interested?” message with a booking link
Just browsing: Add to a general nurture sequence
Negative experience: Alert team for personal outreach
Set up a re-engagement workflow that triggers if a contact who chatted with your AI agent has not converted within 48 hours. A well-timed follow-up message can recover leads that would otherwise go cold.
In your AI agent, configure actions to apply specific tags based on conversation context
In Automation > Workflows, create workflows with the trigger Tag Added
Filter by the specific tag name
Build your automation sequence
This creates a loosely coupled system where the AI agent and workflows communicate through tags. The advantage is flexibility — you can modify workflows without changing the AI agent, and vice versa.
Complete example showing a Conversation AI action integrated into a lead qualification workflow
Keep AI agents focused on conversation. Let workflows handle the backend automation. Do not try to make the AI agent do everything.
Use clear handoff points. Define exactly when and why a workflow should trigger. Ambiguous trigger conditions lead to unreliable automation.
Always include fallbacks. If a workflow fails or returns unexpected data, the AI agent should have a graceful response rather than breaking the conversation.
Test the complete chain. Testing the AI agent alone or the workflow alone is not enough. Test them together with realistic data.
Minimize the number of workflow triggers per conversation to conserve AI credits
Use tags for simple routing and reserve workflow triggers for complex multi-step processes
Add short wait steps between bot-to-workflow triggers to prevent race conditions
Monitor workflow execution logs alongside conversation logs to debug issues
Avoid creating circular triggers where a workflow triggers a bot that triggers the same workflow. This can create infinite loops and consume your AI credits rapidly. Always include exit conditions in your chains.
