What it does
Assigns incoming Front conversations based on actual agent workload (complexity of open tickets, response time, conversation status) rather than simple round-robin or ticket count, preventing agent burnout and improving response quality.
Why I recommend it
Round-robin assigns tickets evenly by count, but not all tickets are equal. One agent might have 3 complex enterprise issues while another has 5 simple password resets. Smart workload balancing keeps your team from burning out.
Expected benefits
- Even workload distribution across support team
- Faster resolution times
- Reduced agent burnout and turnover
- Better service quality on complex issues
How it works
New conversation arrives in Front -> calculate current workload score for each agent (open tickets × complexity + age) -> assign to agent with lowest workload score -> update agent capacity in real-time.
Quick start
Start by tagging tickets as “simple,” “medium,” or “complex” manually for a week. Calculate average resolution time for each. Use this data to build a basic workload score, then route new tickets based on total workload points.
Level-up version
Use AI to predict ticket complexity from the first message, factor in agent expertise (technical issues to technical agents), track real-time agent status (in meeting, lunch break), and auto-reassign if workload becomes unbalanced.
Tools you can use
Support: Front, Zendesk, Intercom, Help Scout
Automation: Zapier, Make, n8n
Analytics: custom workload dashboard
Scheduling: Google Calendar for agent availability
Also works with
Support platforms: Gorgias, Freshdesk, HubSpot Service Hub
Team chat: Slack for status updates
CRM: GoHighLevel for customer priority data
Technical implementation solution
- No-code: Front rule triggers on new conversation -> Zapier fetches each agent’s open ticket count and tags -> calculate workload score in Google Sheets -> assign via Front API to lowest score.
- API-based: Webhook on new conversation -> query Front API for all agent conversations -> calculate workload (count × complexity factor × age multiplier) -> assign to agent with min score via PATCH request.
Where it gets tricky
Defining “workload” accurately (what’s more work: 10 simple tickets or 2 complex ones?), handling agent-specific expertise, accounting for after-hours and PTO, and avoiding gaming the system.
