Welcome to the MonDive

Today in MonDive, we’re looking at AI agents and how they can help you handle everyday tasks, get work done across your apps, and support your business.

We’ll explore what AI agents are, how they differ from LLMs, and why they matter, then look at practical examples, how to build your first agent, and where one could fit into your own life or company.

Alright, let’s dive in.

Put Your Own AI Agent to Work

AI can give you an answer. Your agent can get the work done.

How much time do you spend managing your calendar, sorting emails, following up with leads, updating spreadsheets, and preparing reports? Your own AI agent can handle those tasks for you.

You decide what it handles and stay in control. Start with one routine task, see how much time it saves, and bring that same efficiency to more of your work.

What Is an AI Agent?

Think of an AI agent as an assistant you can hand an assignment to. You explain what you need, and it works out the steps to get it done.

Imagine you want to order a pizza within a certain budget. An agent connected to the right services could check nearby restaurants, find an option that matches your preferences, and prepare the order for your approval. If the first restaurant is closed, it could look for another instead of stopping and waiting for you to explain what comes next.

That is the basic idea: you give it a goal, and it can work through the steps, checking the results and adjusting along the way. It does this through tools that let it search, read information, or take actions. How agents work.

Of course, it needs the relevant information and permission to use those tools. It does not automatically know your preferences or have access to your accounts. You decide what it can handle independently and when it should come back to you.

AI Agents vs. LLMs: What’s the Difference?

A large language model, or LLM, is the technology behind many AI conversations. It processes your request, reasons over the information available, and generates a response.

An agent often uses an LLM to decide what to do. The surrounding software gives it access to tools and lets it carry out those decisions. So an agent does not replace the LLM; it builds on it.

There is also a middle ground worth understanding: an AI workflow. This connects AI with tools, but follows steps and rules that someone has already defined.

Here is how the three approaches might help with meetings:

The distinction is who determines the path. In a fixed workflow, the path is designed in advance. In an agent, the model can make decisions along the way within defined limits. A long automation with many steps is not necessarily an agent. Anthropic’s explanation.

Why Learning AI Agents Matters

How much work have you put off because you didn’t have the time, the budget, or someone to help?

That is one reason to learn about AI agents. They can make work more manageable when doing everything yourself takes too long and hiring help isn’t practical.

For your personal work, that could mean getting help with research, organizing information, or keeping a project moving alongside your other responsibilities. You still guide the work, but you don’t have to carry out every step yourself.

For businesses, the possibility is exciting: growing revenue without automatically needing a 100-person team to handle the work. Small teams can take on more, while larger companies can give employees more time for work that needs their experience and judgment.

Take Anthropic, the company behind Claude. Its support team reports that an AI agent resolves nearly two out of every three customer-support conversations, leaving people to focus on the complicated cases. That is a real example of agents helping a team handle more work.

Learning AI agents helps you recognize where that kind of help could fit into your own life or company. You learn what to hand over, what to check, and where a person is still needed.

You don’t need to become an expert overnight. Start with one useful task, and build from there.

Real-World AI Agent Use Cases

1. Carry Out Research, From Search to Report

A research agent can take a question, decide what to search for, and use a connected research tool to gather information from the web. It then turns those findings into a report with links to the original sources.

In this video, Nate Herk tests a research agent with a question about improving sleep. The agent uses Perplexity to search the web and returns a detailed report, with the sources available to inspect.

The request starts in n8n, where the agent has access to the research tool. Nate doesn’t have to collect articles and paste them into the conversation first; the connected tool handles that research.

This is a simple example of giving an agent a question and letting it gather the information needed to answer it. The findings still need review, but the source links make that easier.

2. Handle Email and Calendar Requests Across Your Apps

An AI personal assistant can work directly with your inbox, contacts, and calendar, using the access you give it.

The personal assistant in this video receives its instructions through Telegram and carries them out in Gmail and Google Calendar.

First, Nate asks for a summary of his emails. The assistant retrieves unread messages and brings back the important details, including a project update and an invoice discrepancy.

He then asks it to email Colin about a project. The assistant uses the connected contact information, writes the message, and sends it. In another demonstration, he requests a meeting with Colin at 5 p.m., and the event appears in his calendar.

These are separate requests handled through the same assistant. The video shows the resulting email and calendar entry, so you can see the work completed beyond the conversation.

3. Answer Customer Calls and Update Bookings

A voice agent can handle a customer’s request while working with the business’s calendar and customer records.

This video opens with two demonstration calls to an AI receptionist for a car-detailing business.

During the first call, Nate introduces himself as a new customer. The receptionist collects and confirms his details, creates a customer record, checks availability, and books an interior-detailing appointment for 8 a.m.

He calls again and asks to move the appointment an hour later. This time, the receptionist finds his existing record, checks the calendar, and moves the booking to 9 a.m. after getting his confirmation.

The calendar and customer records are shown updating during the calls, with a call summary logged afterwards. The agent is handling both the conversation and the actions needed to complete the booking.

4. Pick Up Assignments From a Shared Task List

An agent can receive work through a project board rather than waiting for someone to keep a chat open.

Once an assignment is marked ready, it reads the brief, consults the available project information, posts its plan, and starts working. For a content assignment, that can mean preparing video ideas, title options, and outlines informed by previous work and current trends.

As it works, the agent updates the task’s status and returns the output to the same place for review. Feedback can send the assignment back for another pass, with the earlier context still attached.

This gives a team a practical way to delegate: the agent carries out the assignment, while people review the result and decide what is ready to use.

How to Build Your First AI Agent

Start with one small job you would genuinely like to hand over. You don’t need to build an assistant that handles everything on day one.

A visual builder such as n8n lets you connect an AI model to your apps using blocks on a screen. For a first project, the basic process looks like this:

• Choose what starts the agent. This could be a message from you or a schedule, such as every morning at 8 a.m.

• Connect an AI model. It interprets the request and decides which available tools to use. You can also add memory if it needs to remember earlier messages.

• Give it the tools it needs. Connect your calendar, email, spreadsheet, or another source of information. Only allow the access required for its job.

• Write clear instructions. Explain what it should accomplish, which information to consider, and what the finished result should look like. Set boundaries for actions that need your approval.

• Test and adjust. Run it yourself, check the output, and fix mistakes before relying on it regularly.

The video below shows how to build a trail-planning agent in n8n. It:

• Checks your calendar, the weather, and air quality.

• Picks a suitable route from a spreadsheet of saved trails.

• Emails a recommendation that fits your available time.

Follow the walkthrough to understand the setup, then adapt it to your own task. Some connected services may require payment.

Want an AI agent built for your personal workflow or business? 

Book a call with Smarter with AI.

Where Could an AI Agent Help You?

Look back at your last week. What kept taking up your time? What did you put off because you had more important work to finish?

That is a useful place to look for your first agent.

• For your personal life: Consider the admin that follows you around—gathering information, tracking updates, and keeping plans organised. Look for something you regularly need help keeping up with.

• For your work: Notice where you spend time collecting information from different apps before you can make a decision. An agent could handle that preparation while you review the findings.

• For your business: Ask your team which recurring tasks slow them down. Where do requests pile up? Which jobs require someone to repeatedly check, copy, or pass information along?

Not every task needs an agent. The best candidate is one that takes meaningful effort, needs some judgement, and has a result you can check.

Judge its value by what changes: less time spent, fewer missed tasks, or a shorter wait for customers—not how complicated the setup looks.

Already thinking of a task you’d like to hand over?

Book a call with Smarter with AI to discuss an agent built around your needs.

Know someone who may be interested?