Lesson Overview

AI agents are designed to think, remember, and act. To accomplish this, every AI agent relies on three main components: the brain, memory, and tools. The brain is typically a large language model such as ChatGPT, Claude, or Gemini. This is what allows the agent to reason, plan, and generate language. Without this core, there wouldn’t be meaningful conversation or decision-making.

Memory is what lets an agent recall past interactions. It can remember previous steps in a conversation or pull up information from documents, databases, or other sources. By using memory, agents provide more relevant and accurate responses, improving over time as they “learn” from experience.

Tools are how agents interact with the outside world. Tools come in many forms, from retrieving data, taking action (like sending an email), to orchestrating more complex sequences by calling other agents or triggering workflows. Whether accessing popular services like email or more advanced APIs, tools extend an agent’s ability far beyond text.

Understanding these components is fundamental, whether you're evaluating, building, or simply using AI agents. The concepts you learn here apply widely—from basic chatbots to advanced automations in business or research.

Who This Is For

This lesson is designed for anyone new to AI agents or looking to clarify how agents actually process, remember, and interact.

• Educators wanting to explain AI basics
• Product managers evaluating AI-powered solutions
• Business professionals seeking to automate everyday tasks
• Developers starting to explore agent platforms
• Researchers examining practical uses for agents
• Anyone curious about the parts that make AI agents work

Where This Fits in a Workflow

When working with or setting up AI agents, knowing their core components is one of the first and most important steps. Before customizing or connecting an agent to different apps or data, you need to understand what each part does:

- If you’re building a chatbot for customer service, you’ll see where the brain provides answers, memory keeps context, and tools allow actions (like looking up order status).

- When automating personal tasks, such as sorting emails or updating a calendar, you’ll map out which tools connect to each service, and how memory improves the workflow.

Clear knowledge of each component makes later configuration and troubleshooting much easier, whether you’re building a solution from scratch or adapting an existing one.

Technical & Workflow Benefits

Earlier approaches to bots or automations often handled one thing at a time—either processing text, fetching data, or sending updates. Modern agents, structured around brain, memory, and tools, bring all this together in every conversation or task. This leads to more natural interactions.

For example, agents powered by large language models can understand complex requests (the brain), remember earlier parts of a conversation or past actions (memory), and act immediately by connecting to tools such as sending an email or updating a database. This structure creates smoother experiences: fewer errors, faster task completion, and more flexible automation.

Using these components, anyone can build solutions that are adaptable and much easier to scale, without being boxed in by manual or single-purpose bots.

Practice Exercise

Pick one real-world task you perform regularly—such as scheduling a meeting, finding information in your email, or organizing a to-do list.

1. Break down the task into three parts: What decisions or communication does it involve (brain)? What information needs to be remembered or referenced (memory)? What external actions or apps are required (tools)?
2. Write one sentence for each part explaining its role in your task.
3. Ask yourself: How would combining these three elements in an AI agent make the task faster or more accurate?

Reflect on where you see the biggest opportunity for improvement.

Course Context Recap

This lesson is part of the “Introduction to AI Agents” course, providing the foundation for understanding how modern agents work. Previous lessons introduced what AI agents are and what makes them different from traditional software. Up next, you’ll start learning how these components are connected and used in practical agent setups. To continue building your AI knowledge, watch the next lesson or explore the full course for a complete picture.