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Overview
| Field | Value |
|---|---|
| Market | Israel - Healthcare Services |
| Vertical | Healthcare |
| Company | Maccabi (מכבי שירותי בריאות) |
| Model | gpt-realtime-2025-08-28 |
| Persona | Female, Professional, Empathetic |
| Language | Hebrew |
| Architecture | Single-skill focused agent |
| Channel | Voice |
Skills
maccabiDoctorAppointment - End-to-end doctor appointment scheduling and management including verification, search, booking, and appointment retrieval.Tools
| Tool | Purpose |
|---|---|
| verify_patient() | Validates patient identity before allowing appointment operations |
| find_slots() | Searches available appointment slots by specialty, doctor, and location |
| book_appointment() | Books the selected appointment slot for the verified patient |
| get_future_appointments() | Retrieves the patient’s upcoming scheduled appointments |
Prompting Techniques
AI-Assisted Prompt Development
This agent’s prompt was developed entirely using GPT 5.2 Codex, including all iterative refinements during the testing phase. This approach demonstrated the viability of AI-assisted prompt engineering for production agents.Core Flow
The agent follows a structured, step-by-step approach:- Patient Verification - Verify identity before any appointment operations
- Intent Classification - Determine if the patient wants to schedule, view, or manage appointments
- Information Collection - Gather specialty, preferred doctor, and location preferences
- Slot Presentation - Present available slots in a clear, organized manner
- Booking Confirmation - Confirm the selected slot and complete the booking
Key Principles
- Collect all required details (specialty, location, date preferences) before searching for slots
- Present slots in a digestible format, not overwhelming the patient
- Confirm booking details before finalizing
- Offer to help with additional appointments after completing a booking
Lessons Learned
What Worked
Clear Sequential Flow:- Step-by-step collection of patient details prevented confusion
- Patients understood what information was needed and why
- Reduced back-and-forth in conversations
- Patients accepted the verification step when explained clearly
- Requiring verification before any appointment operations ensured data security
Challenges
Rapid Development Timeline:- The agent was developed and deployed within a single day
- Tight timeline required efficient tooling and clear scope definition
- The Wonderful CLI significantly accelerated the development process
- Multiple available slots required careful UX consideration
- Balancing completeness of information with conversational brevity
- Handling scenarios with no available slots gracefully