This project focuses on building an enterprise-grade AI research and decision orchestration system using PydanticAI and LangGraph. It manages the complete workflow from user query intake to multi-step research, reasoning, and final decision generation. Type-safe agents perform data retrieval, analysis, validation, and synthesis using structured outputs. LangGraph orchestrates agent execution, branching logic, retries, and feedback loops. State persistence ensures context continuity across long-running workflows, while observability tracks performance and failures. The project reflects real-world enterprise knowledge workflows used in consulting, strategy, and analytics teams.

This project focuses on developing an automated document review and compliance management system using agentic workflows powered by PydanticAI and LangGraph. It handles the end-to-end lifecycle from document ingestion to classification, risk evaluation, compliance checks, and approval recommendations. Business rules and structured schemas validate extracted data, enforce regulatory constraints, and flag exceptions. LangGraph manages multi-step decision paths, human-in-the-loop approvals, and escalation logic. The system demonstrates enterprise-level governance, auditability, and traceability commonly used in legal, finance, and compliance departments.

This project focuses on building an intelligent customer support case resolution platform using coordinated AI agents with PydanticAI and LangGraph. It manages the complete support lifecycle from ticket intake to categorization, root-cause analysis, solution generation, and closure. Agents interact with knowledge bases, tools, and APIs while producing validated, structured responses. LangGraph orchestrates escalation paths, SLA-based routing, and human approval checkpoints. Persistent state enables context retention across conversations and resolutions. The project mirrors real-world enterprise support operations used by SaaS, telecom, and large service organizations.