Study your old phone calls before you build a voice agent: have AI mine past call transcripts for the questions and objections that come up, then give the phone agent the full property facts, one goal (a booked consultation) and clear rules for handing over to a person: AI agent prompt
Most phone agents are built from guesses about what callers ask. On his project page, developer Alessio Ragni describes two AI agents running in production for an Italian real-estate auctions consultancy whose contacts went from about 1,250 a week in August to over 3,600 in September 2026. Before designing the phone agent, Carla, he analysed 79,857 past calls (1.1 million turns). Carla says she's an AI at the start of each call, gives property facts, answers objections from a knowledge base, calls back people she promised to call and books a phone consultation with a human consultant; she's measured on one thing, how many calls end in a booking. A second agent, Andrea, works WhatsApp, email and website chat and proposes properties one at a time. He reports 50.5% of calls ending in a booking when the agent had the full property sheet versus 18.2% with only a saved search, and about 14 cents per minute of voice. Handovers are explicit: urgent (auction close, no free slot), 'we want to bid' (three questions, then straight to the head of the consultancy), call me back, and staff tasks; when a consultant writes in a thread, the agent goes quiet for 24 hours, 48 hours or for good. These are the builder's own figures for one business. Brokerages and property teams can copy the approach with their own call records, if callers were told calls may be recorded and local rules allow it.
The prompt
1. Export transcripts or notes from your last few hundred calls (remove personal details first). 2. Ask your AI: "Read these call transcripts. List the 20 most common questions and the 10 most common objections, with how often each comes up and the best answer my team gave. Note which facts callers needed that we didn't have to hand." 3. Build a one-page fact sheet for each property or service (price, dates, terms, what's included) so the agent answers from facts, not guesses. 4. Ask: "Write instructions for a phone agent whose only goal is to book a consultation with a human. It must say it's an AI in its first sentence, answer only from the fact sheets, and hand over to a person in these cases: [urgent deadline, ready to buy or bid, asks for a human, admin task]. For each handover, write the 2 or 3 questions it asks first and the note it leaves for the team." 5. Test it on 20 role-play calls, count how many end in a booking, fix the gaps, and keep measuring that one number after launch.
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Works with: ElevenLabs, Twilio, Supabase, Gemini, WhatsApp, CRM, ChatGPT, Claude
Real-world example: alessioragni.com/projects/agents/
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Before you use it: what to check
Already built into this prompt
- Ask: "Write instructions for a phone agent whose only goal is to book a consultation with a human.
Check yourself
- This prompt works on text other people wrote (emails, reviews, messages or web pages). That text can hide instructions aimed at AI, such as "ignore your rules and…". Paste it as material to work on, and never let the AI act on requests inside it, like paying, sharing logins or changing records. How prompt injection works →
- Check every figure, price and total against your own records before anything is sent, posted or booked. A person approves refunds, discounts, payments and price changes.
- Anything legal, tax or compliance-related is a first draft for a qualified person to check. It is not professional advice, and clause references and deadlines must be checked against the source.
- Remove names, contact details, account numbers and other personal data the task doesn't need before you paste, and check your AI tool's data and training settings.
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