AI Agent

What it is

An AI Agent is a significant evolution from traditional chatbots. While a common chatbot (like ChatGPT in its base version) is limited to answering questions based on text, an agent has the capacity to act. In a business context, we can think of the agent as a digital collaborator that not only knows how to explain a process but can also execute it.

Unlike traditional automation software (like Zapier), which follows rigid "if this, then that" rules, the agent uses the reasoning of a Large Language Model (LLM) to decide the best path to follow. It has the autonomy to break down a complex goal into smaller steps, evaluate its success at each stage, and correct its route if something fails.

How it works

The operation of an agent is based on four fundamental pillars:

1. Profiling and Planning: The agent receives an identity (e.g., "You are a logistics manager") and a goal. It uses the LLM to create an action plan, deciding which subtasks are necessary.
2. Tools (Tool Use): This is the distinctive feature. The agent has access to APIs, databases, or third-party software. If it needs to know the status of an order, it doesn't invent the answer; it uses a tool connected to the company's ERP to consult real data.
3. Memory: Agents possess short-term memory (the history of the current iteration) and often long-term memory (through Vector Databases), allowing them to learn from past interactions or maintain context for long projects.
4. Feedback Loop: The agent observes the result of its actions. If it tries to access a file and access is denied, it can read the error, adjust its strategy, and try an alternative approach, such as asking for authorization or searching for the file elsewhere.

When to use

An SME should consider implementing agents when tasks go beyond simple text generation and require integration with operating systems. Ideal scenarios include:

• Multi-step processes: Tasks involving searching for information, analyzing it, formatting it, and sending it to another system.
• Need for real-time data: When answers depend on information that changes constantly (stocks, prices, calendar availability).
• Interaction with external software: Acting on behalf of the user in CRM, billing software, or marketing tools.
• Triaging and routing: Deciding, based on the content of an email, which is the correct department and preparing the necessary notes for problem resolution.

Common errors

The biggest mistake when implementing AI agents is the lack of guardrails (limits). Giving total autonomy to an agent without security restrictions can lead to undesirable executions, such as deleting data or sending wrong messages to customers.

Another frequent error is role ambiguity. If the instructions given to the agent are vague, it can enter an infinite reasoning loop without ever reaching a practical conclusion. It is essential to define what the agent can and cannot do.

Finally, ignoring latency. Since agents need to "think", plan, and then act, the response is not instantaneous. Many companies try to use agents for real-time chats where simple FAQ with RAG would be more efficient and faster.

Practical example for an SME

Let's imagine a Portuguese construction materials distribution company. Currently, when a customer requests a quote, an employee has to: 1. Read the email; 2. Consult the stock in the ERP; 3. Check the customer's personalized price list; 4. Calculate shipping; 5. Generate a PDF and send it.

An AI Agent configured for this task can automate the full cycle:

• Receives the email and identifies the requested products.
• Activates the database query tool to check stock.
• If there is a stockout, the agent autonomously searches the catalog for an equivalent product.
• Uses a route calculation tool to estimate delivery costs.
• Drafts the quote and saves it as a draft in the CRM, notifying the salesperson for final validation before sending.

Here, the agent didn't just "talk" about the quote; it interacted with the ERP and CRM to perform the heavy administrative work.

Frequently Asked Questions

Q: What is the difference between an Agent and a Chatbot? A: The chatbot focuses on conversation and information retrieval. The agent focuses on task execution and interaction with other software tools to complete a goal.

Q: Can an AI agent make financial decisions alone? A: Technically yes, but in practice, there should always be a "human-in-the-loop". The agent prepares the action and the human approves it, especially in transactions or critical data changes.

Q: Do I need to program to have an agent in my company? A: There are 'low-code' platforms that allow creating agents by connecting blocks, but for robust integrations with internal SME systems, technical intervention is usually required to configure APIs securely.

Q: Are AI agents expensive to maintain? A: The cost depends on the volume of tasks. Since agents make multiple calls to the language model to "think", token consumption is higher than a simple chat, but the return on investment comes from saving human labor hours.

• 01An agent that monitors complaint emails and automatically schedules resolution meetings in the calendar.
• 02A purchasing assistant that compares prices between suppliers and generates purchase orders for approval.
• 03A sales agent that qualifies leads on LinkedIn and updates the CRM with a conversation summary.
• 04An inventory manager that detects low stock and suggests orders based on sales history.

Want to use AI Agent in your company?

30 minutes, free, no commitment. We map where it fits.

Free AI diagnosis