--- title: "AI Agent for Vehicle Insurance: Quote Generation, Claims Intake, and Policy Questions" description: "Build an AI agent for vehicle insurance that generates coverage quotes, handles claims intake with proper classification, collects required documents, and answers policy questions accurately." canonical: https://callsphere.ai/blog/ai-agent-vehicle-insurance-quote-generation-claims-intake-policy category: "Learn Agentic AI" tags: ["Vehicle Insurance", "Claims Processing", "Quote Generation", "InsurTech", "Python"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-06T01:02:45.634Z --- # AI Agent for Vehicle Insurance: Quote Generation, Claims Intake, and Policy Questions > Build an AI agent for vehicle insurance that generates coverage quotes, handles claims intake with proper classification, collects required documents, and answers policy questions accurately. ## AI Agents in Vehicle Insurance Vehicle insurance involves complex workflows: generating quotes based on driver profiles and vehicle details, processing claims that range from minor fender-benders to total losses, answering policy questions about coverage limits and deductibles, and routing escalations to the right department. These interactions follow predictable patterns that an AI agent can handle efficiently. The agent we build will generate personalized insurance quotes, walk customers through claims intake with proper incident classification, collect required documentation, and provide accurate answers about policy coverage. ## Driver and Policy Data Models ```python from dataclasses import dataclass, field from datetime import date from enum import Enum from typing import Optional class CoverageType(str, Enum): LIABILITY = "liability" COLLISION = "collision" COMPREHENSIVE = "comprehensive" UNINSURED_MOTORIST = "uninsured_motorist" PIP = "personal_injury_protection" class ClaimType(str, Enum): COLLISION = "collision" THEFT = "theft" WEATHER = "weather" VANDALISM = "vandalism" GLASS = "glass" ANIMAL = "animal_strike" HIT_AND_RUN = "hit_and_run" @dataclass class DriverProfile: driver_id: str name: str age: int years_licensed: int violations_3yr: int accidents_5yr: int credit_tier: str zip_code: str @dataclass class VehicleProfile: vin: str year: int make: str model: str trim: str annual_mileage: int ownership: str # owned, financed, leased anti_theft: bool = False garage_kept: bool = False @dataclass class Policy: policy_number: str driver: DriverProfile vehicle: VehicleProfile coverages: dict[str, dict] premium_monthly: float deductible_collision: float deductible_comprehensive: float effective_date: str expiration_date: str ``` ## Quote Generation Tool The quote engine scores risk factors and calculates premiums: ```mermaid flowchart LR CALLER(["Policyholder or Lead"]) subgraph TEL["Telephony"] SIP["Twilio SIP and PSTN"] end subgraph BRAIN["Insurance AI Agent"] STT["Streaming STT Deepgram or Whisper"] NLU{"Intent and Entity Extraction"} TOOLS["Tool Calls"] TTS["Streaming TTS ElevenLabs or Rime"] end subgraph DATA["Live Data Plane"] CRM[("CRM and Notes")] CAL[("Calendar and Schedule")] KB[("Knowledge Base and Policies")] end subgraph OUT["Outcomes"] O1(["Quote captured"]) O2(["Claim opened in core"]) O3(["Licensed agent handoff"]) end CALLER --> SIP --> STT --> NLU NLU -->|Lookup| TOOLS TOOLS CRM TOOLS CAL TOOLS KB NLU --> TTS --> SIP --> CALLER NLU -->|Resolved| O1 NLU -->|Schedule| O2 NLU -->|Escalate| O3 style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a style NLU fill:#4f46e5,stroke:#4338ca,color:#fff style O1 fill:#059669,stroke:#047857,color:#fff style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937 ``` ```python from agents import function_tool BASE_RATE = 120.00 # Monthly base @function_tool def generate_insurance_quote( driver_age: int, years_licensed: int, violations_3yr: int, accidents_5yr: int, vehicle_year: int, vehicle_make: str, vehicle_model: str, annual_mileage: int, zip_code: str, coverage_level: str = "standard", ) -> str: """Generate a vehicle insurance quote based on driver and vehicle details.""" rate = BASE_RATE # Age factor if driver_age 65: rate *= 1.15 # Experience factor if years_licensed 10: rate *= 0.85 # Mileage factor if annual_mileage > 15000: rate *= 1.10 elif annual_mileage str: """File a new insurance claim with incident details and classification.""" description_lower = incident_description.lower() # Auto-classify claim type if any(w in description_lower for w in ["hit", "crash", "rear-end", "collision"]): claim_type = ClaimType.COLLISION elif any(w in description_lower for w in ["stolen", "theft", "broke into"]): claim_type = ClaimType.THEFT elif any(w in description_lower for w in ["hail", "flood", "storm", "tree"]): claim_type = ClaimType.WEATHER elif any(w in description_lower for w in ["deer", "animal", "bird"]): claim_type = ClaimType.ANIMAL elif any(w in description_lower for w in ["windshield", "glass", "window"]): claim_type = ClaimType.GLASS elif any(w in description_lower for w in ["vandal", "keyed", "graffiti"]): claim_type = ClaimType.VANDALISM else: claim_type = ClaimType.COLLISION claim_number = f"CLM-{policy_number[-4:]}-{incident_date.replace('-', '')}" priority = "HIGH" if injuries_reported else "STANDARD" required_docs = ["Photos of damage", "Driver's license"] if police_report_number: required_docs.append("Police report copy") if other_party_involved: required_docs.append("Other driver's insurance info") required_docs.append("Other driver's contact details") if claim_type == ClaimType.THEFT: required_docs.append("Police report (required for theft)") if injuries_reported: required_docs.append("Medical records / bills") lines = [ f"=== Claim Filed ===", f"Claim Number: {claim_number}", f"Policy: {policy_number}", f"Type: {claim_type.value.replace('_', ' ').title()}", f"Priority: {priority}", f"Date of Incident: {incident_date}", f"Location: {incident_location}\n", f"Required Documents:", ] for doc in required_docs: lines.append(f" - {doc}") if injuries_reported: lines.append("\nIMPORTANT: This claim involves injuries and has been escalated to a senior adjuster.") lines.append(f"\nA claims adjuster will contact you within 24 hours.") return "\n".join(lines) ``` ## Policy Lookup Tool ```python POLICIES = { "POL-AA-12345": Policy( policy_number="POL-AA-12345", driver=DriverProfile("D-001", "Maria Gonzalez", 34, 16, 0, 0, "excellent", "90210"), vehicle=VehicleProfile("1HGCG5655WA027834", 2024, "Honda", "Accord", "Sport", 12000, "financed", True, True), coverages={ "liability": {"limit": "100/300/100"}, "collision": {"deductible": 500}, "comprehensive": {"deductible": 250}, "uninsured_motorist": {"limit": "100/300"}, }, premium_monthly=142.50, deductible_collision=500.0, deductible_comprehensive=250.0, effective_date="2026-01-15", expiration_date="2026-07-15", ), } @function_tool def lookup_policy(policy_number: str) -> str: """Look up policy details including coverages, deductibles, and premium.""" policy = POLICIES.get(policy_number.upper()) if not policy: return f"Policy {policy_number} not found. Please verify the number." lines = [ f"=== Policy Details ===", f"Policy: {policy.policy_number}", f"Insured: {policy.driver.name}", f"Vehicle: {policy.vehicle.year} {policy.vehicle.make} " f"{policy.vehicle.model} {policy.vehicle.trim}", f"VIN: {policy.vehicle.vin}", f"Period: {policy.effective_date} to {policy.expiration_date}\n", f"Coverages:", ] for cov, details in policy.coverages.items(): name = cov.replace("_", " ").title() info = " | ".join(f"{k}: {v}" for k, v in details.items()) lines.append(f" {name}: {info}") lines.append(f"\nMonthly Premium: ${policy.premium_monthly:.2f}") lines.append(f"Annual Premium: ${policy.premium_monthly * 12:.2f}") return "\n".join(lines) ``` ## Assembling the Insurance Agent ```python from agents import Agent, Runner insurance_agent = Agent( name="Insurance Advisor", instructions="""You are a vehicle insurance assistant. Help customers: 1. Generate insurance quotes based on their driver profile and vehicle 2. File claims with proper classification and document requirements 3. Look up policy details and explain coverages Always explain coverage terms in plain language. For claims involving injuries, emphasize the importance of seeking medical attention first.""", tools=[generate_insurance_quote, file_insurance_claim, lookup_policy], ) result = Runner.run_sync( insurance_agent, "I hit a deer on Highway 101 last night. My policy is POL-AA-12345. No injuries but significant front-end damage." ) print(result.final_output) ``` ## FAQ ### How do I make the quote engine more accurate? Production insurance rating engines use actuarial tables, territory codes (based on zip code loss history), vehicle symbol ratings (from ISO), and proprietary loss models. Integrate with rating APIs from providers like Guidewire or Duck Creek. The agent collects inputs and calls the rating engine rather than computing premiums directly. ### Can the agent handle policy changes like adding a driver or changing vehicles? Yes. Add endorsement tools that modify an existing policy. Each endorsement type (add driver, change vehicle, adjust coverage) requires specific inputs. The tool should calculate the pro-rated premium adjustment and present it to the customer for approval before applying the change. ### How should I handle sensitive personal information during claims intake? Never store sensitive data like Social Security numbers in conversation logs. Use the agent to collect non-sensitive claim details, then redirect the customer to a secure portal for document uploads and sensitive information. Implement PII detection in the agent's response pipeline to redact any accidentally shared sensitive data. --- #VehicleInsurance #ClaimsProcessing #QuoteGeneration #InsurTech #Python #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/ai-agent-vehicle-insurance-quote-generation-claims-intake-policy