Tour Booking

Sub-agent for outbound listing-office calls to request and confirm property showing slots using a provided call script and structured payloads. Use when a pa...

Overview

Handle the call execution layer for property showing bookings:

1. Build a consistent call prompt from listing and client data.
2. Send outbound call request to ElevenLabs (or dry-run).
3. Normalize call outcome into structured status fields.

Inputs

Each call job should include:

• job_id
• client_name
• listing.address
• listing.office_phone
• preferred_windows_text
• timezone

Runbook

1) Build payload

bash

python3 scripts/prepare_call_payload.py \
  --job /tmp/job.json \
  --output /tmp/call-payload.json

2) Place call

Dry-run (default safe mode):

bash

python3 scripts/place_outbound_call.py \
  --payload /tmp/call-payload.json \
  --output /tmp/call-result.json \
  --dry-run

Live mode:

bash

python3 scripts/place_outbound_call.py \
  --payload /tmp/call-payload.json \
  --output /tmp/call-result.json \
  --live

3) Parse outcome

bash

python3 scripts/parse_call_result.py \
  --input /tmp/call-result.json \
  --output /tmp/booking-outcome.json

Call Guardrails

• State clearly that the caller is an AI assistant calling on behalf of the realtor.
• Ask for available slots inside the requested window first; request alternatives if unavailable.
• Confirm final slot with exact date and local time before ending the call.
• If the office cannot confirm, mark as pending_callback and capture callback requirements.