Project Goal and Business Impact Imagine filing an auto insurance claim. Instead of waiting days for a damage evaluation, photograph the car with your phone and, within minutes, receive a detailed assessment. The primary objective of this project is to drastically improve the efficiency and objectivity of the initial auto insurance claim process. Current methods rely heavily on human adjusters and manual estimates, resulting in delays and potential cost inflation. By deploying a sophisticated Multi-Agent System, the aim is to provide a fastly, data-driven assessment that benefits both the insurer and the customer. The Multi-Agent Architecture At the heart of this solution, there is an orchestrated system of specialized AI agents, each with a distinct role. The architecture follows a sketch where an Orchestrator Agent works as the brain, creating execution plans, managing agent lifecycle, coordinating the execution, and aggregating results into coherent outputs. The Vision Agent, powered by OpenAI GPT-5.2, acts as the system's eyes. It analyzes uploaded damage photos with technical precision, identifying specific damaged parts (bumpers, panels, headlights, etc.), classifying severity levels (minor, moderate, severe), categorizing damage types (collision, scratch, dent, paint damage), and generating detailed technical assessments. Two specialized Cost Estimation Agents run, representing different repair philosophies. The OEM (Original Equipment Manufacturer) Agent focuses on premium repairs using manufacturer-certified parts from authorized dealers, while the Aftermarket Agent explores cost-effective alternatives using quality certified aftermarket parts from independent shops. Both agents are powered by Perplexity's sonar-pro model, which provides access to current market data and pricing information. The Shop Finder Agent searches for repair facilities near the user's location, provides contact information, ratings, and availability, and adapts its search strategy based on the information retrieved. Technical Highlights The system is built in Python, leveraging several key technologies. The Gradio framework provides an intuitive web interface for image upload, location input, and real-time results display. OpenAI's GPT-5.2 handles computer vision tasks. Perplexity's sonar-pro model accesses current market data for repair costs and local business information. A sophisticated state management system provides each agent with memory of past interactions, confidence scores to assess decision quality, performance tracking to optimize the system, and autonomous decision-making based on context. The application provides comprehensive, auditable car damage analysis, with every step explicitly recorded. Generative AI vs. Manual/Traditional Tools While traditional automated tools rely on rigid, rule-based computer vision and static databases, this Multi-Agent System introduces a modular reasoning layer that bridges the gap between raw data and decision-making. According to the industry research from McKinsey (2025) the agentic workflows reduce claim cycle times from days to seconds with consistency in claim evaluations. Traditional tools are often "black boxes" or monolithic scripts, instead this modular architecture give the opportunity to develop in the future every task as a swappable module for an hybrid framework where every single agent can be replaced by a non Generative AI tool, for flexible, custom and scalable solution.
The Future of Insurance Claims This multi-agent architecture is a robust, scalable blueprint for automating complex decision-making business processes, such as insurance claims. It leverages the strengths of several large language models (LLMs) and specialized agents to deliver a fast, transparent, and comprehensive output that far exceeds the capabilities of a single model. The project demonstrates practical, real-world applications of multi-agent systems in production environments.