AI support operation
Build the full AI support operation: support agent, Tickets, WhatsApp and Instagram intake, routing, escalation, analytics, customer context, and Max activity.
Use this product state to connect the visible UI to the operational decision the lesson is teaching.
Learn the system by following the product states.
Use the screenshots as the primary map: start with the full context, trace the connected workflow, inspect the focused UI, then compare against the completed operating state.
Customer signalWorkflow pathVisible outcomeUse this product state to connect the visible UI to the operational decision the lesson is teaching.
Customer signalWorkflow pathVisible outcomeThis is the state to compare against when the system is configured, connected, or ready for review.
Summary
Build the full AI support operation: support agent, Tickets, WhatsApp and Instagram intake, routing, escalation, analytics, customer context, and Max activity.
Concepts covered
Step breakdown
- Define the system to deployStart from this Support operating system and confirm the business outcome, source signals, owners, and review gates.
- Create the Frontline assetsBuild the workflow canvas, agent prompt, channels, CRM records or tables, routing logic, and Max Activity evidence described in the blueprint.
- Launch the narrow versionDeploy the smallest reliable version first, keep human review visible, then use activity and analytics to expand the system.
What you will build
A complete AI support operation covering WhatsApp and Instagram intake, Ticket creation and lookup, urgency classification, AI-generated replies for low-risk issues, escalation routing for sensitive cases, and Max Activity for every support interaction.
After deployment: a customer contacts through WhatsApp or Instagram, Frontline identifies them, checks open Tickets, classifies urgency, sends an AI reply or escalates to a teammate, creates or updates the Ticket, and logs the activity — all without manual triage.
When to use this
Your support volume has outgrown manual WhatsApp management but you still need human escalation for complex cases.
Support conversations are not being tracked — no Tickets, no history, no audit trail.
You want AI to handle common, repeatable questions (order status, hours, FAQ) while humans handle billing, complaints, and sensitive cases.
Your support team needs to see customer history before they respond, not after.
System components
Channels: WhatsApp and Instagram connected and mapped to support workflows.
CRM Person record: name, phone or Instagram handle, open tickets, last conversation, support tier.
CRM Ticket record: category, urgency, status, owner, SLA, resolution note.
AI support agent: configured with product knowledge base, response constraints, and escalation triggers.
Knowledge base resource: approved answers for FAQ, product policies, and common support categories.
Conditional Routing: branches on urgency, category, and confidence.
Max Activity: support interaction log, ticket state, resolution or escalation reason, owner note.
Step-by-step implementation
Agent prompt
You are an AI support agent for Frontline. Your job is to help customers resolve support issues using the approved knowledge base.
You will receive: the customer's message, their name, and a summary of any open tickets.
Response rules: Only use information from the knowledge base. Do not invent policies, prices, or timelines. For billing, account changes, or complaints, always escalate.
Output JSON: { response: string | null, action: 'reply' | 'escalate', urgency: 'low' | 'medium' | 'high', category: string, confidence: 0-100, escalation_reason: string | null }.
If action = 'reply': the response field contains the customer-facing message. If action = 'escalate': response is null and escalation_reason explains why.
Workflow logic
Action = reply + confidence ≥ 70: send AI response, create or update Ticket as 'Responded', write Max Activity.
Action = escalate OR confidence < 70: create Ticket as 'Escalated', assign to available support owner, send customer 'we are looking into this' message, write Max Activity with reason.
Urgency = high regardless of action: always escalate. Never send an automated reply to a high-urgency case.
Repeat contact (open ticket exists): route directly to the Ticket owner. Do not restart the AI classification loop.
No reply from support owner within SLA: send customer a follow-up acknowledgment, escalate ticket priority in CRM.
Final operating state
Every customer support message from WhatsApp or Instagram creates or updates a Ticket record with category, urgency, and status.
Low-urgency, high-confidence cases receive an AI reply within seconds. Customers do not wait for a human response for common questions.
Escalated cases are assigned to a specific owner with full context — customer identity, open tickets, conversation summary, and escalation reason.
Max Activity shows every support interaction: message received, agent decision, reply or escalation, and ticket state.
Support analytics show: volume by channel, resolution rate, escalation rate, category breakdown, and SLA compliance.
Troubleshooting
AI replying to cases that should be escalated: tighten the escalation rules in the agent prompt. Add explicit categories that always escalate regardless of confidence.
Person not found on Instagram messages: Instagram DMs may not include a phone number. Use Instagram handle as the Person identifier instead.
Ticket not updating after AI reply: verify the Ticket update node is on the reply branch and receives the Ticket ID from the lookup node.
Support owner not receiving escalation: check that the owner assignment field is populated before the notification fires.
Knowledge base answers are outdated: update the Knowledge Base documents in Studio → Knowledge Bases → edit the relevant document.
Operational playbook
Use AI support operation as part of the Frontline Solutions Support operating loop: inspect the current product state, confirm the source context, and decide what should happen next.
The goal is not to memorize screens. The goal is to understand how the product surface supports repeatable work, AI assistance, and accountable handoff.
Best practices
Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.
Prefer narrow, inspectable setups over broad automation. Teammates should be able to explain why the system took an action from the visible product state.
Platform layers involved
Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.
Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.
Outcome metrics
Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.
The metric should reflect the business outcome, not only whether the automation ran.
Agent Builder visual map
FAQs
What does AI support operation teach?
Build the full AI support operation: support agent, Tickets, WhatsApp and Instagram intake, routing, escalation, analytics, customer context, and Max activity.
How should teams use this lesson?
Use it as an implementation guide: create the assets, connect the systems, verify the completed state, and operate the blueprint with review gates.
Which Frontline products are involved in this solution?
Most solution playbooks connect Studio workflows, Channels, CRM records, AI agents, and Max Activity. The business outcome is the entry point; the platform layers make it operational.
How should we decide whether to automate this use case?
Automate when the path is repeated, has clear source context, needs consistent follow-up, or benefits from AI classification, routing, summaries, or structured capture. Keep human review where judgment or risk is high.
What should be visible before this goes live?
Verify the workflow trigger, CRM context, channel permissions, AI agent instructions, handoff owner, logs, and Max Activity output so the team can trace what happened.
How do we keep the customer experience personal?
Use CRM context, conversation history, and approved message patterns. AI should use relevant customer memory, not generic copy, and workflows should escalate when context is missing.
What is the best first version of this playbook?
Start with one channel, one workflow, one owner group, and a narrow success metric. Expand only after logs, activity, and customer-facing outputs are trustworthy.