You are helping me build the following AI agent workflow.

## Goal
Qualify Insurance Leads Voice: New insurance quote? The agent sends an SMS, calls urgent leads immediately for qualification, or adds standard leads to a nurture drip.

## Specification
- What it does: Receives insurance quote requests via webhook and normalizes prospect data. Routes urgent leads to an AI voice agent for immediate qualification while sending standard leads into a nurture SMS drip sequence. Automates the initial contact and data capture process without human intervention.
- Trigger: Triggered by an event (Event · on new quote request)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - n8n: Workflow orchestration and logic
  - Aloware: SMS delivery and AI voice qualification calls
  - LLM: AI Voice Agent for lead qualification

## Known pitfalls, handle each one explicitly in your implementation
1. SMS compliance requires explicit consent handling, so add a logic check before sending any nurture messages to avoid legal risks.
2. Data normalization must handle inconsistent date formats from webhooks, so add a standardization step before creating contacts in Aloware.

## Reference implementation
https://n8n.io/workflows/15030 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/qualify-insurance-leads-with-ai-voice/ via usecasesforagents.com