Understanding AI Agents: From Basics to Multi-Agent

This tutorial provides a comprehensive overview of AI agents, covering definitions, agentic workflows, multi-agent architectures, and practical applications. It also includes a guide on creating AI agents without coding and discusses future opportunities in the field of AI agents. The video is designed to equip viewers with the knowledge needed to understand and potentially build AI agents, culminating in an assessment to test comprehension.

Key Points

1. AI agents differ from non-agentic workflows by employing a circular, iterative process.
2. There are four main agentic design patterns: reflection, tool use, planning and reasoning, and multi-agent systems.
3. Multi-agent systems can be designed using various patterns such as sequential, hierarchical, hybrid, parallel, and asynchronous.
4. AI agents can be created without coding using tools like n8n.
5. Every SaaS company may have a corresponding AI agent company, presenting significant business opportunities.

Step-by-Step Tutorial

Introduction

In this tutorial, you will learn about AI agents, their functionalities, and how to create your own AI agent workflows without any coding. We will cover the definition of AI agents, the difference between agentic and non-agentic workflows, and explore multi-agent architectures. By the end, you'll understand how to design and implement AI agents using no-code tools, and you'll be equipped with the knowledge to start building your own AI agent solutions.

Prerequisites

• Basic understanding of AI and machine learning concepts
• Access to a computer with internet connectivity
• An account on platforms like Telegram and Google (for calendar access)
• Familiarity with no-code workflow automation tools like n8n

Steps

Step 1: Understanding AI Agents

AI agents are entities that can independently perform tasks or make decisions based on their programming and available data. They differ from non-agentic workflows, which are linear and direct, by using a circular, iterative process to refine outputs. Non-agentic workflows are straight start-to-finish, while agentic workflows involve thinking, researching, outputting, and revising until a satisfactory result is achieved. The ultimate goal is to reach fully autonomous AI agents, though currently, we focus on agentic workflows with some autonomous elements.

Notes:

• Agentic workflows are more effective for complex tasks because they allow for iterative improvement.
• Fully autonomous AI agents are not yet widely available but are a future goal.

Step 2: Exploring Agentic Design Patterns

There are four main agentic design patterns: Reflection, Tool Use, Planning and Reasoning, and Multi-Agent Systems. Reflection involves an AI reviewing and improving its own outputs. Tool Use involves equipping AI with tools like web search or code execution to enhance task execution. Planning and Reasoning allows the AI to break down tasks and select appropriate tools. Multi-Agent Systems use multiple AIs with specialized roles to collaborate on tasks, often yielding better results than a single AI. Remember the mnemonic 'Red Turtles Paint Murals' for these patterns.

Notes:

• Multi-Agent Systems can be more effective for complex tasks as they mimic teamwork in humans.
• Each pattern serves a specific purpose in improving AI agent capabilities.

Step 3: Creating a Single AI Agent Workflow

To create a single AI agent workflow, you need to define its task, expected answer, model, and tools. Use the mnemonic 'Tired Alpacas Mix Tea' (Task, Answer, Model, Tools) to remember these components. For example, a travel planner AI agent might have the task of planning a 3-day trip to Tokyo on a budget, with the answer being a detailed itinerary, using a model like Anthropic's Claude, and tools like Google Maps and Skyscanner.

Notes:

• Each component of the AI agent is crucial for its effectiveness.
• Choose tools that are relevant to the specific task the AI agent is designed to handle.

Step 4: Setting Up a Multi-Agent System

Multi-agent systems involve multiple AI agents working together. There are several design patterns: Sequential (where agents pass tasks in an assembly line), Hierarchical (with a manager agent overseeing sub-agents), Hybrid (combining sequential and hierarchical), Parallel (agents working on different parts simultaneously), and Asynchronous (agents executing tasks at different times). For example, in a hierarchical system, a manager AI agent could delegate tasks to sub-agents for market analysis, customer sentiment, and internal metrics, then compile the results.

Notes:

• The choice of design pattern depends on the task complexity and the need for real-time processing.
• As complexity increases, so does the potential for chaos, requiring careful management.

Step 5: Implementing a No-Code AI Agent Workflow with n8n

You can create a multi-agent system using no-code tools like n8n. Start by setting up a workflow trigger (e.g., Telegram message). Use a switch to handle different input types (text or voice). For voice input, transcribe it using OpenAI and then process the text. Define the AI agent with its task, answer, model, and tools. For instance, create a Telegram-based AI assistant that prioritizes tasks and schedules events on Google Calendar. Use n8n to connect these components into a workflow.

Notes:

• n8n allows for flexible and powerful workflow creation without coding.
• Ensure you have the necessary permissions to access tools like Google Calendar.

Step 6: Exploring Opportunities for AI Agents

AI agents have significant potential in various industries. A key insight is that for every SaaS company, there will be a corresponding AI agent company. To find inspiration for building your own AI agent, consider existing SaaS companies like Adobe, Microsoft, or Shopify, and think about how you can create an AI agent version of their services. Use tools like ChatGPT to brainstorm ideas based on popular SaaS companies.

Notes:

• The potential for AI agents is vast, particularly in automating and enhancing existing services.
• Start by identifying a niche SaaS service and envision how an AI agent could improve it.

Conclusion

In this tutorial, you've learned the basics of AI agents, including their design patterns and how to create agentic workflows using no-code tools like n8n. You now have the knowledge to start building your own AI agents, whether for personal use or to explore business opportunities. As AI technology continues to evolve, the potential for AI agents to revolutionize various industries is immense. Keep experimenting and learning to stay at the forefront of this exciting field.