Customer Profile
A technology-driven business looking to automate repetitive tasks such as research, email drafting, scheduling, and coding using an advanced AI-powered multi-agent system.
Challenge
Traditional AI systems often lack adaptability, real-time collaboration, and confidence-based decision-making, leading to inefficiencies and requiring significant human intervention. The client needed a scalable and intelligent automation solution to optimize productivity and streamline workflows.
Result
Biz4solutions created MultiTaskerAI. The solution employs multiple specialized AI agents that work collaboratively to handle different tasks efficiently instead of relying on a single AI model. The system assigns tasks to dedicated agents, each designed for a specific purpose, ensuring seamless automation for research, communication, scheduling, and coding. The Supervisor Agent acts as the central AI brain, dynamically delegating tasks based on user needs and refining responses based on confidence levels.
Overview
MultiTasker AI is a system where a Supervisor Agent intelligently delegates tasks to specialized AI agents, ensuring efficient task automation. The Research Agent conducts web searches and summarizes findings, providing users with relevant and concise information. The Email Agent drafts, reviews, and sends professional emails, streamlining communication. The Schedule Agent manages meetings and calendar events, optimizing time management. The Code Agent writes, executes, and debugs Python scripts, enhancing coding efficiency. Additionally, when AI confidence is low, the system defers tasks to Human Assistance, ensuring accuracy and reliability. This collaborative framework enables real-time decision-making, adaptability, and productivity across various domains.
Developmental Challenges
Our team came across some developmental challenges as mentioned below:
- Ensure the Supervisor Agent efficiently delegates tasks without bottlenecks.
- Prevent conflicts when multiple AI agents attempt to process the same request.
- Rate-limiting issues by Email Agent when sending multiple emails simultaneously via external APIs.
- Determine when to involve human intervention was challenging, as incorrect confidence thresholds could lead to excessive AI reliance or unnecessary human input.
How did we resolve these development challenges?
We implemented a priority-based scheduling algorithm that dynamically assigns tasks to AI agents based on workload and priority levels and a mutex locking mechanism to prevent agents from processing duplicate tasks simultaneously. We integrated rate-limiting mechanisms and email batching to prevent API throttling issues for the Email Agent. We implemented confidence scoring with adaptive thresholds, ensuring human intervention is requested only when AI confidence drops below an optimal threshold and designed a real-time notification system, allowing seamless communication between AI agents and human users for necessary approvals or corrections.
