View Agent
Open in Wonderful Platform
Overview
| Field | Value |
|---|---|
| Market | Israel - Healthcare Services |
| Vertical | Healthcare |
| Company | Maccabi (מכבי שירותי בריאות) |
| Model | GPT Realtime 28-08-2025 |
| Persona | Female, Professional, Empathetic |
| Language | Hebrew Only |
| Channel | Voice |
Skills
maccabiServiceLocator - Finds and locates Maccabi healthcare services including clinics, doctors, pharmacies, urgent care centers, labs, and dental servicesKey Tools
| Tool | Purpose |
|---|---|
| identify_user_request_service_locator() | Classifies user requests as FAQ/service/out-of-scope; identifies medical terms using LLM |
| pick_term_service_locator() | Resolves ambiguity when multiple service terms are identified |
| note_taker_service_locator() | Saves search filters (doctor name, gender, language, time preferences) |
| predict_location_service_locator() | Predicts cities/streets/POIs from Hebrew text using Google Maps + fuzzy matching |
| identify_location_service_locator() | Confirms and stores resolved location coordinates for search |
| search_service_locator() | Executes search with all gathered context; returns formatted results |
| forward_service_locator() | Forwards call to human agent with auto-determined extension |
| external_message_service_locator() | Sends SMS with service details (name, address, hours, phone) |
| trolling_counter_service_locator() | Silent counter for inappropriate behavior; forwards after threshold |
| on_start_service_locator() | Session initialization; handles blocklist and authentication |
Prompting Techniques
1. No Echoing Rule
Critical instruction to never repeat user’s words. No Echoing Rule (CRITICAL): NEVER repeat, summarize, or paraphrase what the user just said. Do not restate any details from the user’s request (names, locations, services, dates). Jump directly to action or result.2. Filler Restrictions
Prevents information leakage in acknowledgments. Filler Restrictions: Fillers must be generic and short. They must NOT include: doctor/entity names, locations, service types, dates/times, or any other detail the user mentioned. The purpose of a filler is only to signal you’re working - not to confirm understanding.3. Voice-First Result Presentation
Present one result at a time. Present one result. STEP 1: Give name + address briefly. If unsure it’s what they want, ask: ‘זה מה שחיפשת?’ / ‘זה מתאים לך?’ STEP 2 (if confirmed): First, offer to send full details via SMS. STEP 3 (if they decline SMS or ask verbally): If they asked for specific info (hours/phone/referral) → give only that.4. Strict Grounding
No hallucination allowed. Strict Grounding: You are prohibited from inventing or guessing information. Your response must be based solely on the data returned by the tools.5. FAQ-First Classification
Comprehensive FAQ database with LLM routing The agent uses a large FAQ database with keywords and semantic matching. LLM classifies requests into: FAQ, Service (search), or Out-of-Scope. FAQ responses take priority to handle common requests without search.Lessons Learned
What Worked- State machine architecture - Breaking the flow into discrete steps (identify request → identify location → search) with clear transitions improved reliability and debugging
- Short base prompt - Keeping the base prompt concise while moving detailed instructions into tool descriptions reduced latency and improved response consistency
- KV store for context - Using key-value storage to persist conversation state (identified terms, coordinates, notes, search history) enabled stateless tool execution while maintaining context across the conversation
- Dynamic instructions in tools - Returning agent_instructions from tools based on results allowed context-aware guidance without bloating the base prompt
- Tools latency - Multiple sequential tool calls (identify → predict location → confirm location → search → format) added latency. Mitigated through parallel API calls, LLM racing (Gemini vs GPT), and code-based formatting instead of LLM formatting
- Agent humanity - Making the voice agent sound natural rather than robotic required careful prompt engineering: no echoing rules, filler restrictions, voice-first formatting (Hebrew words for times/distances), and conversational flow that lets the caller lead