Introduction to AI Agents

01 definition

What is an AI agent?

A chatbot answers a question and stops. An AI agent goes further: you give it a goal such as “find the top 5 competitors and put a summary in a sheet,” and it decides what to do, uses tools to do it, checks the results, and keeps going until the goal is reached or it needs your help.

Chatbot vs workflow vs agent

The agent loop

1. GoalYou describe what you want done.
2. ThinkThe model plans the next step.
3. ActIt calls a tool: search, code, API, browser.
4. ObserveIt reads the tool result and checks progress.
5. Repeat or finishIt loops until done, or hands back to a human.

Key terms

Autonomy

How much the agent decides alone. More autonomy means more power and more risk.

Tool

Anything the agent can use: web search, calculator, code runner, email, calendar, database, another agent.

Function calling

A feature where the model outputs a structured request like “run search with this query,” and your code executes it.

Planning

Breaking a big goal into smaller steps and choosing the order.

Memory

What the agent remembers: the current conversation (short term) and saved facts across sessions (long term).

RAG

Retrieval Augmented Generation. The agent looks up relevant documents before answering, so it uses your real data.

MCP

Model Context Protocol. An open standard that lets AI apps connect to tools and data sources in a consistent way.

Human in the loop

A person approves important actions before they happen.

Guardrails

Rules and checks that limit what an agent can say or do.

Evals

Tests that measure how well your agent performs on realistic tasks.

02 anatomy

What is inside an agent?

03 types

Types of AI agents

Classic types from AI theory

Modern agents you will meet

Real world use cases

04 build guide

How to build an AI agent

Start simple. Many tasks are better solved with a fixed workflow, and you should only add agent style freedom where the path really cannot be known in advance.

1. Define one clear goal

   Write what success looks like. Example: “Read new support emails, find the answer in our help docs, and draft a reply.” A narrow goal is easier to test.

2. Decide: workflow or agent?

   If the steps are known and fixed, build a workflow. If the number and order of steps depends on what the AI finds, use an agent.

3. Choose a model

   Start with a strong model to prove it works, then try a cheaper, faster one for simpler steps to cut cost.

4. Write the instructions

   Give role, goal, rules, output format and when to ask a human. Add two or three examples of good behaviour.

5. Add tools

   Give only the tools it needs. Write a clear description for each, and return short, readable results so the model does not get lost.

6. Add memory and knowledge

   Connect your documents with RAG, and store useful facts so the agent does not start from zero every time.

7. Add guardrails

   Set permissions, a maximum number of steps, spending limits and approval for risky actions like sending money or deleting data.

8. Test with real examples

   Build a small set of realistic tasks with known good answers. Run them after every change and track success rate, cost and time.

9. Deploy, monitor, improve

   Log every step the agent takes. Review failures, fix prompts and tools, and expand its freedom slowly.

The core idea in code

Every agent framework is a version of this simple loop (shown in Python style, simplified):

messages = [system_prompt, user_goal]

while steps < MAX_STEPS:
    reply = model(messages, tools=tool_list)

    if reply.wants_tool:
        result = run_tool(reply.tool_name, reply.tool_input)
        messages.append(result)      # agent observes the result
    else:
        print(reply.text)            # final answer
        break

Common design patterns

05 tool stack

Which tools can we use to build AI agents?

Pick based on your skill level. Features and pricing change quickly, so check each official website before deciding.

No code and low code platforms

Code frameworks and SDKs

Models, data and support tools

06 safety and limits

Risks, limits and best practices

07 roadmap

Your learning path

1. Week 1Learn prompting and how LLMs work. Use a chat assistant daily.
2. Week 2Build a simple AI workflow in n8n, Zapier or Make.
3. Week 3Learn basic Python. Call a model API and use function calling.
4. Week 4Build your first agent loop with one or two tools.
5. LaterAdd RAG, memory, evals, MCP and multi agent designs.

08 faq

Frequently asked questions

Is an AI agent the same as ChatGPT or Claude?

No. Those are chat assistants built on models. An agent uses a model but adds tools, memory and a loop so it can act toward a goal.

Do I need coding to build an agent?

Not to start. No code platforms can build useful agents. Coding gives you more control and lower cost later.

Are agents reliable enough for real work?

For narrow, well tested tasks with human review, yes. For high stakes actions, keep approvals in place.

What is the difference between an agent and automation?

Classic automation follows fixed rules. An agent decides its own steps, so it can handle unexpected situations.

How much does it cost?

You pay for model usage, and sometimes for the platform. Costs grow with the number of steps, so set limits and test with cheaper models.

What is MCP and why does it matter?

It is an open standard for connecting AI apps to tools and data. It lets you reuse the same integrations across different agents and apps.