Introduction to AI Agents: The Guide That Finally Brings Order.

The word agent is everywhere today. On Instagram, TikTok, at every tech conference, and in every post by Google, Microsoft, NVIDIA, or Meta. Anyone trying to enter this world mostly gets confusion: what is an agent, how is it different from a chat, what is a skill, what is a connector, and why is everyone suddenly talking about it. This guide is the written summary of AI Master Club meeting 29, an introduction to AI agents. Its goal is simple: to bring order. By the end of reading this, you will know what an agent is, what parts it is built from, how it works, in what environments it is operated, how much it costs, and how to build your first automated workflow without writing a single line of code.

The bottom line upfront: working today only with a chat is like owning an engine without a car. The chat answers, the agent executes. Anyone working with knowledge, whether in a business or as an employee, can already hand over entire workflows that they currently do manually over and over again to an agent.

The problem: We are the glue between apps

The evolution, in a short time

So what is an agent anyway?

The difference in one line

• Chat: Explains, drafts, suggests, and then waits for your next request. Endless ping pong.
• Agent: Reads the situation, plans, operates tools, and executes actions in the digital world.
• Chat does not initiate. An agent does not stop until the task you defined is completed.
• Chat generates text. An agent generates a result: a file, a sent email, a scheduled meeting, an uploaded video.

The analogy that brings order: The world of agents as a kitchen

The complete dictionary

• Work environment = The kitchen. The place where the agent lives and operates (Claude Code, Codex, Cursor, Antigravity).
• The agent = The chef. The one who actually executes.
• The language model = The chef's brain. It can be replaced, and any agent can be given a different brain.
• The task = The ordered dish. Your prompt = The customer order.
• Documents and data = The ingredients. Everything you provide as context.
• Skills = The cookbook. Fixed work instructions that it pulls out every time.
• Connectors (MCP) = Kitchen appliances and access to the fridge and supermarket. Its hands and feet.
• Memory = Its personal recipe notebook, a file stating who you are and how you work.
• The output = The prepared meal.

This is the most important point in this guide. The agent is not smarter than the chat because its model is better. It is stronger because it was given an environment: files, memory, skills, tools, and permissions. The exact same model, in two different environments, produces completely different results.

An agent is a system, not a model

The agent loop: How it actually works

The five stages of the loop

• Reads the situation · Gathers information on the current state, what it knows and what it is missing.
• Plans · Decides on the next step and builds an action plan.
• Acts · Operates a tool and actually executes an action.
• Verifies · Compares the result against the target you defined.
• Continues or stops · Returns to the loop if not finished, and stops when the task is completed.

When does the agent know it is done

The four work environments where agents are operated

How much it costs

• Claude · $20 a month minimum, Max plans from $100 and up.
• Codex · $20 a month, included in ChatGPT subscription. Pro plans $100 or $200.
• Cursor · $20 a month, $40 per user for teams.
• Antigravity · Free version with daily limits.

Do not waste time on the question of which tool is best. The answer changes every month. What does not change are the work principles: context, skills, connectors, agents, and permissions. Anyone who understands these five moves between tools without a problem.

The five elements that determine everything

1. Context · Who I am and what matters to me

2. Skills · How I want the work to be done

AI Master Club opens the door for you: live meeting every week, recordings, and a community. 47 NIS per month, cancel anytime.

There is a new capability here worth knowing: Record Skill in Claude. You open a screen and microphone recording, execute the process once from start to finish, and at the end the agent summarizes all the steps itself and packages them as a skill. Sometimes it will even tell you that there is no need to click all the buttons because a connector already does it directly.

3. Connectors · What the agent is allowed to touch

The difference between API and MCP confuses many people, so briefly: The API is the system where the tools themselves sit (for example, Google AI Studio, which contains Google image and video models), and that is also where payment and the access key reside. The MCP is the bridge that connects your work environment to that API. You connect once, and from that moment on the agent can use these capabilities within your workflow.

4. Agents and sub-agents · Who executes each part

5. Permissions and proof · When can you trust it

Trust rules for a new agent

• Start with read-only permissions, no changes and no deletions.
• Every email or publication goes through a draft that you approve.
• Limit access: not all capabilities of every tool, only what is required for the task.
• In the initial stage, give only reversible actions. A mistake should not be a catastrophe.
• Ask for proof: a screenshot, the created file, a full activity log.
• Always keep a human in the loop. Especially when starting out.

Treat a new agent like a new employee: on the first day, you do not give them access to the bank account. You start with risk-free tasks, and expand authorities as they prove themselves.

What it looks like when it works

How to choose the first process

Six signs that a process is suitable for an agent

• The process repeats frequently, at least once a week.
• It is built from clear steps that can be explained to a person sitting next to you.
• It consumes real time.
• It requires moving between multiple systems.
• You can check the result with your own eyes.
• A mistake in it is not devastating.

Your exercise · Five minutes

Ready-made prompt for building your first agent

I want to build a first agent that will execute one repeating process in my work for me. The process is: [describe the process in one sentence]. Ask me one question at a time, and at the end assemble a neat skill document for me. These are the questions I want us to go through: 1. What is the desired outcome, and what does a successful output look like. 2. What information and context does the agent need from me. 3. Which tools and connectors should be used. 4. What is it allowed to execute independently. 5. What is it forbidden to execute under any circumstances. 6. When must it stop and ask for my approval. 7. What will prove to me that the task is completed (Definition of Done). At the end of the process, write the skill for me as a neat MD file, with work steps, quality checks, and a permission list.

Invest these five minutes in a single process. This is far better than trying to build an entire system on the first day. A small process that works gives you capability, saves tokens, and creates an appetite for the next process.

Where is this heading

Do not build another bot. Build a new way of working

The five steps in summary

• Choose one repeating process.
• Give it context: who you are, how you work.
• Define a skill: how the process is performed correctly.
• Connect a connector to the relevant tools.
• Define what counts as success, and test.

Frequently asked questions about AI agents

A language model is the brain, it answers questions. An agent is a complete system that receives a goal and executes it: it plans, operates tools, checks itself, and continues until the task is completed. The exact same model, inside an equipped environment, produces a completely different result.

No. In environments like Claude Code and Codex, the agent writes the code for you. You describe what you want in plain language, and it executes. The required knowledge is knowledge of workflows, not programming.

MCP is the bridge connecting the work environment to an external tool. The API is the system where the tool itself sits, and that is also where payment and the access key reside. You connect the work environment via MCP to the API, and from that moment on the agent can use the capabilities of that tool.

Yes. An agent can operate sub-agents and even manage an entire team, where each agent has its own role, files, tools, and language model. You can give the main agent a smart and expensive model and the sub-agents cheap and fast models.

A skill is an instruction file that explains to the agent how to perform a certain process repeatedly: steps, templates, examples, and quality checks. You can write it together with the agent, download a ready-made one from the web, or record the process once and let the agent package it on its own.

There is no fixed answer and it changes every month. If you have a ChatGPT subscription, start with Codex at no extra cost. If you want the environment that started it all, Claude Code. The principles are identical across all tools, making the transition between them easy.

Yes, if you work correctly. Start with read-only permissions, request a draft before any send, provide only reversible actions, and ask for proofs and an activity log. Do not start with sensitive financial or medical information.

If until today you felt this world was one big mess, that was exactly the goal of the guide: to break it down into parts. Now all that is left is to choose one process, and give your first agent a task. Start small, test, and expand.

Frequently Asked Questions

What is the difference between a language model and an agent?

A language model is the brain, it answers questions. An agent is a complete system that receives a goal and executes it: it plans, operates tools, checks itself, and continues until the task is completed. The exact same model, inside an equipped environment, produces a completely different result.

Do you need to know how to code to work with agents?

No. In environments like Claude Code and Codex, the agent writes the code for you. You describe what you want in plain language, and it executes. The required knowledge is knowledge of workflows, not programming.

What is the difference between API and MCP?

MCP is the bridge connecting the work environment to an external tool. The API is the system where the tool itself sits, and that is also where payment and the access key reside. You connect the work environment via MCP to the API, and from that moment on the agent can use the capabilities of that tool.

Can an agent operate other agents?

Yes. An agent can operate sub-agents and even manage an entire team, where each agent has its own role, files, tools, and language model. You can give the main agent a smart and expensive model and the sub-agents cheap and fast models.

What is a skill and how do you create one?

A skill is an instruction file that explains to the agent how to perform a certain process repeatedly: steps, templates, examples, and quality checks. You can write it together with the agent, download a ready-made one from the web, or record the process once and let the agent package it on its own.

Which environment is most recommended to start with?

There is no fixed answer and it changes every month. If you have a ChatGPT subscription, start with Codex at no extra cost. If you want the environment that started it all, Claude Code. The principles are identical across all tools, making the transition between them easy.

Is it safe to give an agent access to my systems?

Yes, if you work correctly. Start with read-only permissions, request a draft before any send, provide only reversible actions, and ask for proofs and an activity log. Do not start with sensitive financial or medical information.