03. Anatomy of an Agent: How to Build a Digital Golem?

The Legend of the Golem: The Earliest “Programming”

In 16th-century Prague legends, a Rabbi crafted a giant figure out of river clay—the Golem.

The Golem had no soul and couldn’t think. But the Rabbi placed a parchment with a sacred Hebrew word (Shem) in its mouth, and it would suddenly open its eyes, obeying commands to fetch water, chop wood, or protect the community. If you removed the parchment, it turned back into a pile of lifeless dirt.

This is the earliest concept of an “Agent”: a physical entity with no self-awareness but the ability to execute instructions strictly.

The AI Agents of 2026 are strikingly similar to the Golem. If we “dissect” a modern agent, we find three precise components: The Brain, The Notebook, and The Hands.

1. The Brain: LLM and the “Stochastic Parrot”

The Golem’s brain was the Rabbi’s scroll; the Agent’s brain is the Large Language Model (LLM). GPT-4, Claude 3.5, or DeepSeek play this role.

But you need to understand how this brain works to know why it sometimes acts foolishly.

Why do AI Hallucinate?

Think of a game of “Word Association.” If I say “The cat sat on the…”, you likely say “mat.”

The essence of an LLM is a super-complex “Word Association” machine. It doesn’t understand “Truth”; it only understands Probability. When you ask it a question, it calculates: “In all of human history’s text, what are the most likely words to follow this prompt?”

This explains Hallucination: If you ask: “How did Cinderella win the Super Bowl?”

• Its brain has no such fact.
• But its probability model says “Super Bowl” is usually followed by “Quarterback” or specific game descriptions.
• So, it might confidently invent a story about Cinderella throwing a 50-yard touchdown. It isn’t “remembering”; it is “predicting.”

To make this hallucinating brain reliable, we need the second component.

2. The Notebook: Context & RAG

A Golem is usually forgetful. To make an Agent smart, we give it a “Notebook.”

Closed-Book vs. Open-Book Exams

Using ChatGPT alone is like a student taking a Closed-Book Exam.

• He relies only on what’s in his head (training data). If he forgets, or if it’s new information (like your company’s internal policy), he has to guess.

RAG (Retrieval-Augmented Generation) is like letting that student take an Open-Book Exam.

• When the Agent receives a question, it doesn’t rush to answer.
• It first turns around and “flips through books” in an external knowledge base (like your company’s PDF library).
• It finds the relevant paragraph and copies it into its “Notebook” (Context Window).
• Finally, it answers your question based on the notebook: “According to page 3 of the handbook, reimbursement requires a manager’s signature.”

This is why modern Agents can handle personal tasks: Because they’ve read your notebook, not just their training data.

3. The Hands: Tools & MCP

This is what turns AI into an Agent: Giving it Hands.

Previously, if you asked AI: “Check my bank balance,” it would say: “I can’t, I’m not connected to the internet.”

Today’s Agent has a “Multipurpose Utility Belt.” When it realizes it needs to check a balance, it autonomously calls a bank’s API.

MCP: The Universal Plug for Robots

There’s a cool new concept here: MCP (Model Context Protocol).

Previously, giving an Agent tools was tedious. If you wanted it to connect to Google Drive, you wrote one set of code; for Slack, another. It was like traveling the world with a bag full of different power adapters.

MCP is the USB-C of the AI world.

• As long as your software (GitHub, Notion, Google) supports the MCP standard, the Agent can “plug in” to read data or send commands.
• This means future Agents can move freely between all your apps, becoming a true “Digital Butler.”

4. A Mental Experiment: Building an Agent on Paper

To truly understand how an Agent works, let’s play a game. No coding required. We will simulate a “Travel Planning Agent” manually.

Task: User says, “Book me a cheap flight to London for tomorrow.”

The Agent runs a loop called ReAct (Reason + Act):

Loop 1

• Thought: User wants to go to London tomorrow. I need to know today’s date to find “tomorrow.”
• Action: Call tool Get_Current_Date().
• Observation: Tool returns 2026-02-07.

Loop 2

• Thought: Today is Feb 7, so tomorrow is Feb 8. I need to find flights from the user’s location to London. I don’t know the user’s location.
• Action: Call tool Get_User_Location().
• Observation: Tool returns New York.

Loop 3

• Thought: Route: NYC -> London. Date: 2026-02-08. Goal: Cheap.
• Action: Call tool Search_Flights(from="NYC", to="London", date="2026-02-08", sort="price_asc").
• Observation: Tool returns: [6 AM: $400, 2 PM: $1200, 10 PM: $450].

Loop 4

• Thought: Found them. The 6 AM flight is cheapest ($400), but might be too early. The 10 PM flight is $450. I should present both.
• Final Answer: I found a few options. The cheapest is at 10 PM for $450. There is an even cheaper one at 6 AM ($400), but it’s very early. Should I book the 10 PM one for you?

Summary

This is the “mental activity” of an Agent. It’s not magic; it’s a diligent employee who constantly mutters to itself, looks up info, and corrects its plan.

• Brain (LLM): Reasoning and planning.
• Notebook (RAG): Knowledge and memory.
• Hands (MCP): Action and execution.

In the next chapter, we’ll see how these “Digital Golems” are creating an “End-to-End” revolution in fields like coding, research, and law.