Building a 20 AI Agent Team

Introduction

• Purpose: To demonstrate how to build a 20 AI agent team for managing and automating tasks across a tech stack.
• Capabilities: Integrates with various platforms such as WhatsApp, LinkedIn, email, calendar, Slack, and voice calling.
• Components:
  • Project management tools (CRM, Notion, Google Docs, Google Drive)
  • Research agents for lead/topic research
  • Content agents for social media and blog publishing

Key Features and Capabilities

• Communication Integration:
  • Access to messaging and scheduling through WhatsApp, Slack, LinkedIn, etc.
• Project Management:
  • CRM updates, Notion for content calendar, Google Docs for documentation
• Research and Content Creation:
  • Researching new leads
  • Automated content creation and publishing

Workflow Examples

1. Flight Booking
   • Find flight options, create a Google Doc, send via WhatsApp
2. Calendar Management
   • Reschedule meetings, update calendar
3. Lead Management
   • Research leads, update CRM, notify team
4. Content Creation
   • Write and post blogs and social media content

System Architecture

• Director Agent: Main contact point, delegates tasks
• Manager Agents: Oversee specific areas such as communication, project management, research, and content
• Sub-Agents: Specialized agents for specific platforms/tools

Automation and Scheduling

• Daily Tasks: Automate repetitive tasks using scheduled messages
• Voice Interaction: Send requests via WhatsApp voice messages

Setup Overview

• Technologies Used: Relevance AI for agent setup, Make.com for integrations
• Multi-Agent System: Designed for complex workflows
• Tools and Platforms: Includes Google SERP API for travel, native integrations for communication and project management tools

Challenges and Solutions

• Task Delegation: Clear task delegation to prevent errors
• Contextual Understanding: Provide clear context to agents
• Reliability: Multi-layered agents for task verification

Future Enhancements

• GPT-01 Model Integration: Use for planning and query breakdown
• Scheduling Improvements: Automate routine interactions more efficiently

Conclusion

• Potential: Demonstrates the potential of AI agent teams in automation
• Resources: Templates and community support available for replication
• Further Learning: Additional tutorials and resources available

Additional Notes

• System setup: Breakdown includes triggers, manager, sub-agents, and specific tools
• Programming Tips: Use of detailed SOPs and guidelines for reliability
• Community Resources: Access to templates and one-on-one support

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End of Lecture Notes

• For further details, templates, and community access, refer to the provided resources.