TICKET MANAGEMENT

Auto-assign new Front conversations by agent capacity and timezone

When a new conversation lands in a Front shared inbox, route it to the available agent with the lowest open load whose working hours cover the current time.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerevent
Steps7
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew conversation in Front shared inboxFront
  • ActionLoad agent roster (timezone + shift hours)PostgreSQLPostgres
  • LogicKeep only agents currently on shift
  • ActionQuery open-conversation count per on-shift agentPostgreSQLPostgres
  • LogicPick agent with lowest open load
  • ActionAssign conversation in FrontFront
  • OutputWrite assignment + load snapshot to audit logPostgreSQLPostgres

What it does

Every new unassigned conversation in a Front shared inbox is automatically routed to the best-fit teammate. "Best fit" means an agent who is on shift right now (based on their timezone and working hours) and currently carries the fewest open conversations, so load stays even and replies stay fast.

When to use it

Use it when a support team spans multiple regions and you want round-the-clock coverage without a lead manually dragging tickets. It replaces brittle Front rules that ignore real-time workload and shift boundaries.

How it works

  1. 1A new inbound conversation enters the watched Front inbox and fires the trigger.
  2. 2The flow reads each agent's shift window and timezone from a Postgres roster table.
  3. 3A logic step filters to agents who are on shift at the conversation's arrival time.
  4. 4It queries current open-conversation counts per on-shift agent and picks the lowest.
  5. 5The conversation is assigned to that agent in Front.
  6. 6The pick, candidate pool, and load snapshot are written to a Postgres audit log for later fairness review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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