Multi-Agent Personal Assistant & Time Organizer

Overview

The project involved building 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. By using a Supervisor Agent to orchestrate specialized sub-agents, the platform autonomously handles complex workflows like research and meeting coordination while maintaining human-in-the-loop security.

Achievements

Successfully reduced operational costs and LLM token consumption by 40–60% through a custom multi-layer caching infrastructure. The system reclaimed an estimated 10–15 hours per week for users by automating routine coordination and research tasks. Additionally, the implementation achieved sub-second response times for cached queries, significantly improving the 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 across personal and professional platforms.
• 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 that 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.