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Field service dispatch recovery

Re-plan disrupted field visits within skills, SLA, travel, and customer constraints.

What this workflow does

Re-plan disrupted field visits within skills, SLA, travel, and customer constraints. It validates identity and permissions, simulates the bounded action, and executes only the approved change with verification and rollback evidence.

What you gain

Dispatcher-approved schedule and customer notifications

What the AI agent changes

How this worked before

API and RPA automations could repeat known clicks, but they were brittle when the interface or situation changed.

What the AI agent changes

The agent replans a changing schedule across technician skills, parts, travel time, service levels, and customer windows.

Agent trigger

A technician cancels, a job overruns, or an urgent case enters the queue.

Inputs

  • Process context: jobs, skills, location, SLA, parts, shifts, and customer windows
  • Approved policies, ownership, and exception rules

Agent flow

  1. 1

    Verify identity, entitlement, and the current policy version.

  2. 2

    Simulate the requested action and calculate its bounded effects.

  3. 3

    Prepare a constraint-valid recovery schedule with side effects and rollback conditions.

  4. 4

    Execute the approved action, verify the result, and log the change.

Human decisions

After step 3

The accountable process owner approves a constraint-valid recovery schedule.

Outcome

  • Dispatcher-approved schedule and customer notifications
  • Evidence, exceptions, and audit trail

Guardrails

  • Block execution when identity, evidence, or rollback conditions are incomplete.
  • Require fresh approval for irreversible or high-value actions.

Risks and mitigations

An incorrect or over-scoped action can change a material record.

Use least-privilege tools, dry runs, hard limits, and post-action verification.

Sources and evidence