AI agents: how they’re transforming work routines

Asking a virtual assistant to summarize an email is one thing. But asking it to monitor your inbox, identify urgent messages, check your calendar, and automatically respond to simple cases is another. This second scenario defines an AI agent—a type of system that’s becoming increasingly common in the daily lives of businesses and professionals.

What is an artificial intelligence agent?

An agent is a program that uses a language model—like those powering ChatGPT, Gemini, or Claude—to achieve a goal with a degree of autonomy. Instead of answering a single question, it breaks the goal into steps, selects tools, executes actions, and evaluates results before moving forward.

In a technical guide, Anthropic distinguishes workflows from agents. In workflows, the path is predefined by code. In agents, the model itself decides how to conduct the process and which resources to use.

How does an agent work?

The basic cycle involves four components. The language model interprets the goal and performs reasoning. Tools are the granted access points, such as email, spreadsheets, calendar, or internet search. Memory stores the task context, and instructions set limits and quality criteria.

Examples in everyday life

A customer service agent can classify customer messages and answer frequently asked questions. In finance, it can reconcile transactions and flag discrepancies. In marketing, it can monitor brand mentions and generate a daily summary. In all cases, the person defines the goal and reviews sensitive information.

How to create an agent step by step

The first step is to choose a repetitive and well-defined task with a clear start and end, such as generating a weekly report. Broad goals tend to produce inconsistent results.

Next, define the tools and permissions. No-code automation platforms like Zapier, Make, and n8n allow you to connect a model to applications without programming. Those with technical expertise can use libraries like LangChain or the models’ own APIs.

Then come the instructions. A good command describes the goal, the delivery format, the allowed sources, and what to do in case of doubt—such as asking a human for confirmation. Testing with a few real cases before expanding usage helps identify flaws.

Precautions before delegating tasks

Agents can make mistakes and act on incorrect information. That’s why it’s recommended to start with limited permissions, such as read-only access, and maintain human supervision for irreversible actions like payments or customer messages.

It’s also important to ensure data protection. Personal or confidential information should only be shared with tools approved by the company and in compliance with the LGPD. Recording the agent’s actions makes audits and corrections easier.

What to watch for in the coming years

Providers like OpenAI, Google, and Anthropic have been releasing agent-focused features, expanding options for businesses and professionals. For those looking to adopt the technology, the most common advice is to start with small tasks, measure time savings and error rates, and then expand usage.