Multi-Agent Personal Assistant & Time Organizer

Overview

Built a sophisticated personal productivity automation system based on a Multi-Agent Architecture to streamline tasks and professional collaboration. The system integrates with Google Workspace, Jira, Zoom, and Confluence to provide unified task management, automated scheduling, and intelligent email triage. A Supervisor Agent orchestrates specialized sub-agents that autonomously handle complex workflows like research and meeting coordination while maintaining human-in-the-loop security.

Achievements

• Reduced operational costs and LLM token consumption by 40–60% through a custom multi-layer caching infrastructure • Reclaimed an estimated 10–15 hours per week for users by automating routine coordination and research tasks • Achieved sub-second response times for cached queries, significantly improving user experience and decision-making speed

Responsibilities

• Architected a multi-agent graph system using LangGraph, featuring a central Supervisor Agent that intelligently delegates requests to specialized agents for tasks, calendar management, and research
• Developed a bidirectional synchronization engine between Google Tasks and Jira to provide a unified priority view and eliminate manual task duplication
• Implemented an advanced 'LongMemory' system utilizing recursive summarization and context pruning to maintain high-fidelity context across extended sessions without exceeding token limits
• Designed a secure 'Human-in-the-Loop' validation layer for critical email communications, ensuring high-priority responses require explicit approval while routine drafts are handled autonomously
• Integrated comprehensive analytics and ROI tracking to monitor real-time message volume, per- agent token consumption, and 'estimated time saved' metrics

Technologies Used