---
title: "What an agent really is — StudioMeyer Academy"
description: "Not every LLM call is an agent. The distinction matters once you start building seriously."
canonical: "https://studiomeyer.academy/en/lernwege/fuer-entwickler/lektionen/01-was-ist-ein-agent"
language: en
image: "https://studiomeyer.academy/og/academy-og-share.png"
publisher: "StudioMeyer Academy, https://studiomeyer.academy (llms.txt: https://studiomeyer.academy/llms.txt)"
---

## The typical wrong definition

"An agent is an LLM that can do stuff." That's not enough.

An LLM call is: you put in a prompt, get an answer back. That's a function call, not an agent.

An **agent** is a system that:

1. Pursues a **goal** (not just answering a question)
2. **Decides autonomously** which steps to take
3. **Uses tools** to interact with the world (read files, search the web, write code)
4. **Runs in a loop** until the goal is reached or given up on

The loop is the difference. An agent observes, decides, acts, observes again. Many times.

## Simple example

**Not an agent:** "Claude, write me an email to customer XY." → Claude writes an email. Done. One turn.

**Agent:** "Claude, find the 10 biggest customers who haven't ordered in 30 days and send each one a personalized re-engagement email." → Claude has to: read CRM data, filter, sort, look at each customer's history, generate an email, send, write a report. Multiple turns, multiple tools, own planning.

## The four anatomical parts of an agent

**1\. Instructions** (the goal + ground rules) "You're a sales assistant. Find customers X. Rule: no customer with a negative note in their timeline."

**2\. Tools** (what it's allowed to use) List of functions with name, description and input/output schema. E.g. get\_customers, send\_email, log\_action.

**3\. Memory** (what it remembers while the task runs) Short-term: what have I done so far. Long-term: what have I learned from earlier tasks.

**4\. Loop** (the execution loop) The agent runner calls the LLM, reads the decision ("I want to call tool X with input Y"), executes it, returns the result to the LLM, asks again. Until the agent reports "done".

## Who built that

Claude Agent SDK from Anthropic. OpenAI Agents SDK. Langchain Agents. All three do the same thing: a runner that orchestrates tool calls in a loop.

You can build an agent in 50 lines of code. The complexity difference isn't the loop, it's how well you design the four anatomy parts.

## The practical point

Not everything needs an agent. Most tasks can be solved with one LLM call if you build the prompt well. Agents are for tasks with **an unknown number of steps**.

"Write me this email" → no agent needed. "Research market X and give me a report" → agent. The agent has to decide how deep to dig, which sources, when it's done.

## Next lesson

The one pattern 80% of serious agent systems use in 2026: CEO/Worker. One agent that orchestrates other agents. That's Lesson 2.

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For developers · Module 1 of 1

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