LabPilot AI: Multi-Agent Laboratory Assistant

My project is a multi-agent laboratory assistant platform built using Band as the coordination layer. The system addresses a common academic and enterprise workflow problem: transforming experiment requirements into complete, verified, and presentation-ready outputs while reducing manual effort and coordination overhead. The solution consists of three specialized AI agents working collaboratively. The Experiment Analyzer acts as the primary coordinator and domain expert. It receives user requests, understands experiment requirements, generates structured laboratory reports, performs calculations, and creates technical explanations. The Viva Coach specializes in oral examination preparation by generating viva questions, model answers, and conceptual explanations. The Lab Reviewer serves as a quality assurance agent that reviews reports for technical accuracy, formatting consistency, completeness, and compliance with laboratory standards. Band enables these agents to function as a coordinated team rather than isolated chatbots. Agents maintain role specialization, exchange context, perform task handoffs, and contribute their expertise to a shared workflow. The Experiment Analyzer delegates viva preparation tasks to the Viva Coach and validation tasks to the Lab Reviewer, then consolidates all outputs into a final deliverable. The project demonstrates how enterprise workflows can benefit from intelligent agent coordination. Specialized agents divide work, review outputs, and improve quality. The approach reduces errors, accelerates report generation, improves learning outcomes, and provides users with a complete end-to-end solution. By leveraging Band's orchestration capabilities, the platform showcases a practical implementation of collaborative AI systems for educational and technical documentation workflows.