You are helping me build the following AI agent workflow.

## Goal
Full-Time AI Receptionist: An always-on AI agent handles front-desk duties: answering queries, qualifying leads, routing calls, and booking appointments autonomously.

## Specification
- What it does: You get a full-time receptionist that answers customer questions, qualifies leads, routes calls, and books appointments without needing off-hours coverage. This agent uses the AI Receptionist Platform to handle voice interactions autonomously so your front desk runs smoothly around the clock.
- Trigger: Triggered by an event (Event · incoming call)
- Autonomy: Fully hands-off
- Expected setup effort: a few focused days
- Tools/services involved:
  - AI Receptionist Platform: Handling voice calls and routing
  - LLM: Qualifying leads and answering questions

## Known pitfalls, handle each one explicitly in your implementation
1. Latency in LLM inference can cause awkward silences during calls, so implement streaming responses or hold music buffers.
2. Voice recognition errors on complex names can lead to misrouted calls, add a confirmation loop where the agent repeats back critical details.
3. Regulatory compliance requires clear disclosure of AI usage, add an initial greeting that explicitly states the caller is speaking with an automated system.

## Reference implementation
https://www.youtube.com/watch?v=6u9X4VZMGXU (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/full-time-ai-receptionist/ via usecasesforagents.com