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AI agents can seem complicated, but the core structure is actually simple once you break it down. In this Loom video, I explain the basic architecture of an AI agent using a simple diagram so it’s easier to understand how everything connects. https://www.loom.com/share/0233129ce126411ab2a402419db762a6https://www.loom.com/share/0233129ce126411ab2a402419db762a6 An AI agent mainly consists of five key components: 1. LLM (Large Language Model) This is the brain of the agent. It processes different types of inputs like text, images, video, and audio to generate responses. 2. Instructions Instructions guide the behavior of the agent. They define how the agent should respond to users and help reduce hallucinations. 3. Tools Tools allow agents to perform real actions such as interacting with external platforms, sending data, or connecting with services like CRMs and cloud apps. Without tools, agents can only generate responses but cannot execute tasks. 4. Memory Memory helps the agent remember previous interactions and context, making conversations more natural and useful. 5. Knowledge Base This contains the information the agent should know, such as business documents, PDFs, or internal data. All of these components connect together through APIs to create a fully functional AI agent capable of automation and intelligent responses. In the Loom video, I walk through the diagram step by step to make the concept easier to understand. Would love to hear your thoughts or how you’re structuring your AI agents.

Today I built my first AI Voice Agent using ElevenLabs, and instead of one assistant doing everything, I designed 3 AI agents that work together simultaneously like a small team. The system works like this: 1️⃣ Bella (Main Agent) Bella acts as the primary assistant and handles general customer questions or tech-related queries. 2️⃣ Product Specialist Agent This agent explains the products available in the store and provides detailed information to customers. 3️⃣ Stock Specialist Agent This agent checks and informs customers about the current product availability and stock status. So if a customer asks something general, Bella answers. If they ask about products, the product agent takes over. If they ask about availability, the stock agent responds. It’s basically multiple AI agents collaborating together to simulate a real customer support team. Curious to hear your thoughts : What do you think about this setup? How would you improve this kind of AI voice system for a business?