Building a 20 AI Agent Team for Task Automation

Introduction

• Learn to build a complex AI agent system.
• Capable of managing and automating tasks across a tech stack.
• Future of AI agents and automations.

System Overview

• Access to communication channels (WhatsApp, LinkedIn, email, calendar, Slack, voice calling).
• Access to project management tools (CRM, Notion, Google Docs, Google Drive).
• Research agents for topic and lead research.
• Content agents for social media and blog content creation.
• Interaction through voice messages on WhatsApp.
• Automates complex workflows in simple English.
• Example tasks include:
  • Finding flight options, adding to Google Docs, sending to WhatsApp.
  • Scheduling calls and updating calendars.
  • Writing and publishing content on AI news.
  • Researching leads, adding to CRM, notifying team on Slack.

Demo Breakdown

• System capability for task automation via simple requests.
• Examples: scheduling daily tasks, retrieving unread messages, etc.

System Components

• Director Agent: Primary agent for task delegation and communication.
• Manager Agents: Specialized agents for handling communication, project management, research, and content.
• Sub Agents: Handle specific tasks and integrate with tools.

Tools and Integrations

• Multi-layered agent system to limit responsibilities and ensure reliability.
• Examples of tools:
  • Google Search API for flight and hotel options.
  • LinkedIn scraping for lead information.
  • WhatsApp API for communication.
  • Notion for task management and content calendar.

System Setup

• Built using Relevance AI and Make.com for integrations.
• Setup involves defining roles, objectives, and SOPs for agents.
• Importance of context-rich prompts for effective task execution.

AI Agent Interaction

• Breakdown of query processing by Director Agent.
• Delegation to appropriate Manager Agents.
• Ensures quality and accuracy of task outcomes.

Future Enhancements

• Incorporating GPT-4 models for better planning and reliability.
• Scheduling automated workflows using human language.

Conclusion

• System allows comprehensive automation across various platforms.
• The future of AI involves more seamless, language-based task automation.

Additional Resources

• Template available for replication.
• Community access for further learning and support.

Notes

• The flexibility and adaptability of the system make it a robust solution for businesses looking to leverage AI for automation.
• Continuous improvements and fine-tuning are essential for optimal performance.
• The integration of various AI models and tools showcases the potential of AI in handling complex workflows with minimal human intervention.