AI Agents Architecture — Explain Like I'm 5

From Answering to Doing

Asking an AI a question and getting an answer — that’s the easy part. What’s harder: making the AI actually do things in the world based on what it knows.

Think about the difference between:

• “What’s the weather in Tokyo?” → AI gives you an answer
• “Plan my Tokyo trip” → AI searches flights, books a hotel, adds events to your calendar, and emails you an itinerary

The second case requires the AI to take a sequence of actions, check results, adjust its plan, use external tools, and remember what it’s done so far. That’s what AI agents do.

The Three Things Every Agent Needs

A brain (the LLM): The language model that reasons about what to do next. This is like the agent’s thought process — it reads the situation and decides on a plan.

Tools: Things the agent can actually do. Common tools include:

• Web search
• Running code
• Reading and writing files
• Calling APIs (weather, calendar, email)
• Browsing websites

Memory: Keeping track of what’s happened so far. Without memory, the agent would forget what it just did and could loop or contradict itself.

The Basic Loop

An AI agent runs in a cycle:

1. Look at the current situation and goal
2. Think about what to do next
3. Use a tool to do it
4. Look at what happened
5. Repeat until the goal is achieved

This “think → act → observe → think” loop is the foundation of almost all current AI agent architectures. Simple in concept, surprisingly powerful in practice.

One thing to remember: An AI agent is just a language model with tools and a loop — the magic is that the language model can reason about which tools to use in what sequence to achieve goals that weren’t explicitly programmed.