How to create and manage AI agents: a practical guide

While a chatbot answers specific questions, an AI agent can receive a goal, plan the necessary steps, use tools, and complete the task with minimal human intervention.

This distinction is important for those dealing with repetitive tasks or multi-step processes. According to OpenAI, agents are systems that can perform tasks on behalf of users by combining language models, tools, and instructions.

Essential components of an AI agent

The basic architecture consists of three elements:

• Model: the AI component responsible for reasoning and decision-making.
• Tools: resources that enable information retrieval or external actions.
• Instructions: rules that define how the agent should operate.

OpenAI includes these guidelines in its agent-building manual and also recommends setting limits to control the system’s scope of operation.

Step-by-step guide to implementing an AI agent

1. Define a specific task

Start by choosing a clear and measurable goal. Instead of creating a generic agent to assist with work, specify a concrete activity, such as gathering competitor information every Monday or organizing data received in a spreadsheet.

The more precise the expected outcome, the easier it will be to verify whether the agent has completed the task correctly.

2. Write detailed instructions

Next, describe how the agent should act. Instructions may include:

• the goal to be achieved;
• the permitted data sources;
• the desired format for the response;
• how to handle missing information;
• what the agent cannot do.

Anthropic suggests starting with simple structures and increasing complexity only when it brings tangible benefits.

3. Grant access to necessary tools

An agent can only perform actions for which it has the appropriate tools.

Depending on the use case, this may include search engines, files, databases, spreadsheets, internal systems, or APIs. For example, an agent responsible for a weekly report may need to query a database, analyze the results, and generate a document. Without these connections, its capabilities are limited to text production.

4. Establish limits and security rules

Autonomy does not mean a lack of supervision. An agent that can send messages, alter data, or interact with the external world requires security rules.

OpenAI recommends access controls, result validation, and human intervention for high-risk or hard-to-reverse actions. A simple practice is to require prior approval for any action involving financial transactions, content publication, data deletion, or external communication.

The goal is to automate execution without removing the professional’s control over critical decisions.

5. Test before automating

Before releasing the agent to operate autonomously, conduct tests with various scenarios, including normal cases, incomplete information, and instructions that may generate errors.

Observe not only the final result but also where the agent makes mistakes. Based on these tests, instructions and limits can be adjusted.

Anthropic emphasizes the importance of continuously evaluating systems and using checkpoints to confirm that the agent is moving in the right direction.

AI agents do not mean total automation

An agent can take over part of the execution, but this does not mean that all tasks should be handed over to artificial intelligence.

Microsoft’s 2026 Work Trend Index shows that agents are taking on more execution tasks while workers focus more on direction, decisions, and results. The study surveyed 20,000 workers who use AI in ten countries and analyzed productivity signals from Microsoft 365.

In practice, setting up an agent starts with defining the task, followed by instructions and tools, and ends with control and evaluation mechanisms. This process allows testing the technology in specific tasks before expanding its autonomy.