Projects
Allstate Claim Supervisor Agent (Module 7)
This project simulates an intelligent claim supervisor named Anna who manages a team of agents to process auto insurance claims for Allstate. Built with LangChain, LangGraph, OpenAI GPT-4o-mini, and persistent memory, the system evaluates policy status, classifies damage severity, and determines payment outcomes. It features a modular, node-based structure that mirrors real insurance workflows.
Developed for the MSDS 442 course at Northwestern University, this project highlights how AI agents can be orchestrated into production-grade systems that reason over structured data and delegate tasks across teams, moving beyond chatbots to decision-making frameworks.
⸻
Features
- Supervisor Agent: A human-like assistant named Anna delegates tasks to policy, claim, and damage agents.
- Memory Persistence: Each agent stores outputs to local files and vector memory for long-term tracking.
- Damage Classification: Uses structured prompts to label severity as Minor, Moderate, or Major.
- Vector Search Retrieval: Enables retrieval of past decisions and document references.
- Multi-Agent Workflow: Implements LangGraph with clear stages: input verification classification decision.
- BPMN Compatibility: Designed to align with Business Process Modeling workflows and test cases.
⸻
Key Insight
Intelligent agents can be designed not just to chat, but to supervise. This project illustrates how modular LLM agents can handle real business logic, enabling automated workflows for high-volume, rules-based decision environments like insurance.