--- title: "Building a Car Dealership AI Agent: Inventory Search, Test Drive Scheduling, and Finance Quotes" description: "Learn how to build an AI agent for car dealerships that searches vehicle inventory, schedules test drives, and generates finance quotes using tool-calling patterns and structured vehicle databases." canonical: https://callsphere.ai/blog/building-car-dealership-ai-agent-inventory-search-test-drive-finance category: "Learn Agentic AI" tags: ["Automotive AI", "Car Dealership", "Inventory Management", "AI Agents", "Python"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-06T18:40:52.390Z --- # Building a Car Dealership AI Agent: Inventory Search, Test Drive Scheduling, and Finance Quotes > Learn how to build an AI agent for car dealerships that searches vehicle inventory, schedules test drives, and generates finance quotes using tool-calling patterns and structured vehicle databases. ## Why Car Dealerships Need AI Agents Car dealerships handle thousands of customer inquiries every week. Shoppers want to know if a specific model is in stock, whether they can test drive it Saturday afternoon, and what their monthly payment would be on a 60-month loan. Traditionally these questions get routed to salespeople who manually search DMS (Dealer Management System) databases, check calendars, and run finance calculators. An AI agent can handle the entire pre-sales workflow: searching inventory by make, model, year, color, and price range; booking test drive appointments against availability; and generating personalized finance estimates based on credit tier and down payment. The agent connects to real dealership data through tools and returns accurate, structured answers in seconds. ## Designing the Vehicle Database Schema A dealership inventory system needs to capture vehicle details, pricing, and availability status. Here is a practical schema: ```mermaid flowchart LR INPUT(["User intent"]) PARSE["Parse plus classify"] PLAN["Plan and tool selection"] AGENT["Agent loop LLM plus tools"] GUARD{"Guardrails and policy"} EXEC["Execute and verify result"] OBS[("Trace and metrics")] OUT(["Outcome plus next action"]) INPUT --> PARSE --> PLAN --> AGENT --> GUARD GUARD -->|Pass| EXEC --> OUT GUARD -->|Fail| AGENT AGENT --> OBS style AGENT fill:#4f46e5,stroke:#4338ca,color:#fff style GUARD fill:#f59e0b,stroke:#d97706,color:#1f2937 style OBS fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b style OUT fill:#059669,stroke:#047857,color:#fff ``` ```python from dataclasses import dataclass from enum import Enum from typing import Optional class VehicleStatus(str, Enum): AVAILABLE = "available" ON_HOLD = "on_hold" SOLD = "sold" IN_TRANSIT = "in_transit" @dataclass class Vehicle: stock_number: str vin: str year: int make: str model: str trim: str exterior_color: str interior_color: str mileage: int msrp: float selling_price: float status: VehicleStatus features: list[str] image_url: Optional[str] = None ``` In production, this data lives in the DMS. For our agent, we expose it through search tools that query the database with filters. ## Building the Inventory Search Tool The search tool accepts flexible criteria and returns matching vehicles ranked by relevance: ```python from agents import Agent, Runner, function_tool from typing import Optional VEHICLE_INVENTORY = [ Vehicle("STK-1001", "1HGCG5655WA123456", 2026, "Honda", "Accord", "Sport", "Platinum White", "Black", 12, 33500.00, 32200.00, VehicleStatus.AVAILABLE, ["Sunroof", "Heated Seats", "CarPlay"]), Vehicle("STK-1002", "5YJSA1E26MF123789", 2026, "Tesla", "Model 3", "Long Range", "Midnight Silver", "White", 0, 42990.00, 42990.00, VehicleStatus.AVAILABLE, ["Autopilot", "Premium Audio"]), Vehicle("STK-1003", "2T1BURHE0KC987654", 2025, "Toyota", "Camry", "XSE", "Celestial Silver", "Red", 8500, 31500.00, 29800.00, VehicleStatus.AVAILABLE, ["TRD Package", "Panoramic Roof"]), ] @function_tool def search_inventory( make: Optional[str] = None, model: Optional[str] = None, min_year: Optional[int] = None, max_price: Optional[float] = None, color: Optional[str] = None, ) -> str: """Search dealership vehicle inventory by make, model, year, price, or color.""" results = [v for v in VEHICLE_INVENTORY if v.status == VehicleStatus.AVAILABLE] if make: results = [v for v in results if v.make.lower() == make.lower()] if model: results = [v for v in results if v.model.lower() == model.lower()] if min_year: results = [v for v in results if v.year >= min_year] if max_price: results = [v for v in results if v.selling_price str: """Schedule a test drive for a specific vehicle.""" try: dt = datetime.strptime( f"{preferred_date} {preferred_time}", "%Y-%m-%d %H:%M" ) except ValueError: return "Invalid date/time format. Use YYYY-MM-DD and HH:MM." if dt str: """Calculate monthly payment for a vehicle purchase.""" loan_amount = vehicle_price - down_payment if loan_amount <= 0: return "Down payment covers the full vehicle price. No financing needed." monthly_rate = (annual_rate / 100) / 12 payment = loan_amount * ( monthly_rate * (1 + monthly_rate) ** term_months ) / ((1 + monthly_rate) ** term_months - 1) return ( f"Vehicle Price: ${vehicle_price:,.0f}\n" f"Down Payment: ${down_payment:,.0f}\n" f"Loan Amount: ${loan_amount:,.0f}\n" f"Term: {term_months} months at {annual_rate}% APR\n" f"Monthly Payment: ${payment:,.2f}" ) ``` ## Assembling the Dealership Agent ```python dealership_agent = Agent( name="Dealership Assistant", instructions="""You are a helpful car dealership assistant. Help customers: 1. Search for vehicles by make, model, year, price, or color 2. Schedule test drives for available vehicles 3. Calculate finance quotes with different down payments and terms Always be friendly and transparent about pricing.""", tools=[search_inventory, schedule_test_drive, calculate_finance_quote], ) result = Runner.run_sync( dealership_agent, "I'm looking for a white sedan under $35,000. Can I test drive one Saturday at 2pm?" ) print(result.final_output) ``` The agent will search inventory, find the Honda Accord, and offer to book the test drive in a single conversational turn. ## FAQ ### How do I connect this to a real DMS like DealerSocket or CDK? Replace the in-memory inventory list with API calls to your DMS provider. Most modern DMS platforms offer REST APIs. Wrap each API call in a tool function that handles authentication, pagination, and error responses. Cache inventory data with a short TTL to reduce API calls. ### Can the agent handle trade-in valuations? Yes. Add a tool that accepts the customer's trade-in VIN and mileage, then calls a valuation API like Kelley Blue Book or Black Book to return an estimated value. Subtract the trade-in value from the vehicle price before calculating the finance quote. ### How do I prevent double-booking test drives? In production, use a database-backed appointment system with row-level locking or optimistic concurrency control. Check availability inside a transaction and insert the booking atomically. The in-memory approach shown here is for demonstration only. --- #AutomotiveAI #CarDealership #InventoryManagement #AIAgents #Python #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/building-car-dealership-ai-agent-inventory-search-test-drive-finance