AI Agents for Everyone
The Basics of How AI Agents Work
A visual and interactive guide to understanding the fundamentals of AI agent architecture. Dive into the machine and see for yourself.
— 10 min read — 4 interactive simulators
AI Agents for Everyone
A visual and interactive guide to understanding the fundamentals of AI agent architecture. Dive into the machine and see for yourself.
— 10 min read — 4 interactive simulators
What happens when you type a question into ChatGPT? You hit Enter, wait a few seconds, and get an answer. But between the question and the answer, several things happen.
In this article, we're going to open the hood and look inside.
An is much more than a . While a model generates a single response to your message, an agent can:
Try to see the difference between asking someone a question and asking them to solve a problem. The first requires memory. The second requires strategy.
At the heart of every AI agent lies a simple pattern: the . It breaks down into three steps: Think, Act, and Observe. It's a continuous cycle that allows the agent to adapt and respond intelligently.
The agent receives the question and reasons about the course of action. It doesn't just do . It considers which tools are available, what information it needs, and what the best next step is.
Agent thinking:
"The user wants to know the weather in Paris.
I have a Weather API tool available.
I should call it with location='Paris'."
Based on its reasoning, the agent selects a and executes it. This could be a web search, an call, a database query, or any other action.
Tool call: weather_api(location="Paris")
Result: { temperature: 18, condition: "Partly cloudy" }
The agent examines the result. Is it what it expected? Does it need more information? Should it try a different approach?
This is where agents distinguish themselves from simple : they evaluate results and decide what to do next.
Walk through a complete agent loop step by step. Watch how the agent thinks, chooses a tool, executes it, observes the result, then reasons again before composing its final response:
Note: The interactive demos in this article are pre-scripted simulators, not real AI agents. Their purpose is to illustrate the concepts, not to run an actual model behind the scenes.
Notice the two "Think" steps. The agent reasons before acting (to plan) and after observing (to interpret). This double-thinking pattern is what makes agents far more capable than simple request-response systems.
The loop above shows how an agent reasons. But an agent doesn't operate in a vacuum. It needs tools, memory, and instructions to be useful. These are all provided by the agent's designer.
Designer provides
Tools
APIs, functions, services
Memory
History, documents, preferences
Instructions
System prompt, rules, constraints
Agent
Think — Act — Observe
An agent can only use the tools a designer has explicitly made available to it. Want the agent to check the weather? You provide it with a weather API tool. Want it to search the web? You provide it with a search tool. Without these tools, the agent has no way to interact with the outside world. It can only reason with the information already present in its context.
Agents don't "remember" things on their own. It's the designer who decides what context the agent receives: conversation history, relevant documents, user preferences, or any other information. This memory shapes how the agent reasons and what it knows about the current situation.
Beyond tools and memory, the agent receives instructions (often called a ). These instructions, defined by the designer, guide the agent in how it reasons and acts. They can define a tone, constraints, priorities, or rules to follow.
For example, a customer service agent might receive instructions like: "Always respond in French, stay polite, and never issue a refund without supervisor approval." It's thanks to these instructions that the agent knows how to orient its Think-Act-Observe loop. Without them, the model has capabilities, but no direction.
Think of an agent as a skilled employee on their first day. They're capable and can make decisions, but they work with the tools and information they're given. The agent's designer defines this framework: which tools, which memory, which instructions, which constraints. The agent operates within this framework — autonomous in its decisions, but limited to the capabilities it's been provided.
That's why building an agent isn't just about the loop. It's about designing the right environment for the agent to operate in.
What happens when you remove a tool the agent needs? Toggle the tools on and off below to find out:
Notice what happens without the right tool: the agent doesn't fail silently. It improvises — making up data, getting calculations wrong, or falling back on outdated knowledge. The red note under each response reveals the problem. This is the reality of agent development: the agent is only as capable as the tools you give it.
The agent doesn't use a tool because it was told to. It chooses the right tool based on the question.
Ask "What's the weather?" and it picks the Weather API. Ask "What's 2+2?" and it picks the Calculator. Ask "Who won the World Cup?" and it picks Web Search.
The agent chooses the tool, not you. That's the whole point.
It's the that understands function signatures and matches them to the user's intent.
Give it a try. Change the question below and watch the agent select the appropriate tool:
Notice that the agent doesn't just match keywords. It understands the intent behind your question.
When you chat with an AI agent, you see a smooth conversation. Behind the scenes, the agent is doing something far more complex: analyzing your intent, calling tools, processing results, and composing a response.
Type a question and observe both sides simultaneously. The left panel shows what you'd see as a user. The right panel reveals what the agent is actually doing:
This is the key insight: every smooth, conversational AI response is the result of a structured of reasoning and action unfolding in milliseconds. The simplicity of the user experience hides a sophisticated layer underneath.
Let's recap the key concepts:
This article is part of the "AI Agents for Everyone" series. Stay tuned for the next installment.