AI support routing
Use AI classification and workflow routing to send support conversations to the right path, team, or resolution flow.
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
Use AI classification and workflow routing to send support conversations to the right path, team, or resolution flow.
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
An AI-powered support routing layer that classifies every incoming support conversation by intent and urgency, then routes it to the correct resolution path — self-service reply, ticket creation, or human handoff — with a clear audit trail for every decision.
After deployment: no support message sits unclassified. Within seconds of arriving, the message is categorized, the customer is identified, and the right action begins automatically.
When to use this
Your support team spends significant time triaging messages before anyone starts resolving them.
The same support channel receives multiple types of requests (FAQ, billing, technical, complaints) that need different response paths.
You want routing decisions to be explainable — someone should be able to see why a ticket went to billing vs. general support.
Volume has grown to the point where manual routing creates response-time variability.
System components
Channels: WhatsApp or Instagram as the intake channel.
AI classification agent: reads the customer message and returns category, urgency, and routing decision.
CRM Person record: identity confirmation, support tier, history.
CRM Ticket record: created on every inbound message with category, urgency, and routing outcome.
Conditional Routing: branches by category and urgency to the correct resolution path.
Knowledge Base: for self-service resolution of FAQ-category messages.
Max Activity: classification result, routing decision, and action taken for every message.
Step-by-step implementation
Agent prompt
You are a support routing agent. Classify the incoming customer message and decide the correct routing path.
Categories: 'faq' = can be answered from a knowledge base; 'billing' = payment, invoice, charge dispute; 'technical' = product bug or error; 'complaint' = dissatisfied customer; 'other' = does not fit above categories.
Urgency: 'low' = informational or routine; 'high' = blocking issue, strong frustration; 'critical' = threat to cancel, legal mention, public complaint.
Output JSON: { category: string, urgency: 'low' | 'high' | 'critical', routing: 'self_service' | 'billing' | 'technical' | 'senior_support' | 'queue', confidence: 0-100, classification_note: string }.
If confidence < 50, set routing to 'queue' regardless of category.
Workflow logic
Category = faq + confidence ≥ 70: self-service reply path. Send AI reply, create Ticket as Resolved.
Category = billing: billing team path. Always escalate, never auto-reply.
Category = technical: check for existing open technical ticket. If found: add message to existing ticket and notify owner. If not found: create new ticket and assign.
Category = complaint OR urgency = critical: senior support path. High-priority ticket, immediate acknowledgment.
Confidence < 50 OR category = other: default queue. Create ticket, write Max Activity noting low-confidence classification for manual review.
Final operating state
Every inbound support message is classified within seconds and routed to the correct path — no manual triage required.
Ticket records show category, urgency, routing decision, and outcome for every message.
Classification confidence is logged in Max Activity so routing quality can be reviewed and improved.
Support managers can filter tickets by category, urgency, and routing path in CRM.
Troubleshooting
High rate of 'other' classifications: review the unclassified messages in Max Activity. Add the common patterns as new categories or improve the agent prompt with examples.
Billing messages going to self-service: ensure the billing category check happens before the confidence threshold in the routing logic.
Tickets created without category field: verify the Ticket creation node maps the category from the agent output, not from a static value.
Person not found on new contacts: confirm the lookup uses the correct identifier (phone for WhatsApp, handle for Instagram). Create Person if not found.
Operational playbook
Use AI support routing 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 routing teach?
Use AI classification and workflow routing to send support conversations to the right path, team, or resolution flow.
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.