What is an AI agent and what is it for in a business

Key Takeaways

• An AI agent is a system that receives a goal and decides on its own which steps to take and which tools to use to reach it. That autonomy is what separates it from a chatbot or a rule-based automation.
• Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls (Gartner, June 2025).
• Of the thousands of vendors claiming to sell agents, Gartner estimates only about 130 are real. It calls the rest agent washing: assistants, RPA, and chatbots with a new name.
• 40% of respondents at large organizations say they are scaling agents, compared with 22% at smaller ones (McKinsey, The State of AI in 2026, August 2026).
• Many tasks sold today as "agents" are better solved with a fixed workflow that includes an AI step. It's cheaper, more predictable, and easier to control.

Over the past year many software vendors renamed something they were already selling. The chatbot became an agent, the automation became an agent, and so did the email assistant. For a manager who has to decide where to invest, that makes the conversation almost impossible.

This guide explains what an AI agent really is, how it differs from what you already know, what it's useful for in a mid-sized company, and how to tell whether your process needs one or whether something simpler will do. For the big picture, start with our guide to artificial intelligence for businesses.

What is an AI agent?

An AI agent is a software system that uses a language model to decide what to do next, uses tools (searching for information, querying a system, sending an email, updating a record), and repeats that cycle until it reaches a goal. The difference from other forms of AI is who decides the path: in an agent, the model decides, not a programmer who wrote it down in advance.

Anthropic, the company that develops Claude, offers a distinction in its guide Building effective agents that helps a lot. It calls workflows the systems where models and tools are orchestrated through predefined code paths, and agents the systems where the model dynamically directs its own process and tool use. Its advice is to add agentic complexity only when simpler solutions fall short.

Google Cloud defines it in a similar way: AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users, with the ability to reason, plan, remember, and make decisions with a degree of autonomy.

Every agent has five parts, and knowing them helps you ask a vendor good questions:

How is an agent different from a chatbot, an assistant, or an automation?

It differs in how much it decides on its own. A bot follows rules, an assistant suggests and the person decides, an AI automation follows a fixed path with a smart step in the middle, and an agent chooses the path.

Google Cloud's comparison table reaches the same conclusion: the bot follows predefined rules, the assistant can recommend actions but the user makes the decisions, and the agent acts autonomously to reach a goal.

What is an AI agent useful for in a business?

An agent is useful for tasks that have a clear goal but a path that changes every time, and that require checking several sources before acting. If the task is always done the same way, you don't need an agent.

Some uses where an agent makes sense in a mid-sized company:

• Preparing sales meetings. Looking up a prospect in the CRM, on their website, and in past emails, and leaving a summary with the history of the relationship and the points to cover.
• Handling internal requests. Receiving a team question about inventory, an order, or a policy, searching the right systems, and answering, or escalating when it can't find the answer.
• Reviewing documents against criteria. Reading a supplier proposal or a contract and flagging what doesn't meet the company's conditions, quoting the exact text.
• Following up on receivables. Checking which customers have overdue payments, reviewing each one's history, and preparing the right collection message for a person to approve.

Notice that in almost all of these cases a person approves at the end. The agents that work in mid-sized companies usually prepare the work, not close it on their own.

Why are so many AI agent projects canceled?

Because many are launched before anyone knows whether the task needed an agent. Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, and gave three reasons: escalating costs, unclear business value, and inadequate risk controls. In the same analysis it warned that many use cases positioned as agentic today don't require agentic implementations.

Cost is something almost nobody budgets for. An agent calls the model many times for a single task, and every call has a price. In McKinsey's The State of AI in 2026, about 20% of respondents say the cost of running AI, including token consumption, limited how much they use it.

The other problem is agent washing. Gartner estimates that of the thousands of vendors offering agentic AI, only about 130 sell real agents. If you're offered an "agent" that always follows the same path, what you're being sold is automation. It may be exactly what you need, but you should pay for it as such.

How to tell if your company needs an AI agent, step by step

Before hiring an agent, walk the process through these steps. In most cases you'll end up with something simpler and cheaper, and that's a good outcome.

1. Describe the task the way a person does it today

Write down, step by step, what the person doing the task does: what information they receive, which systems they check, what they decide, and what they deliver. If you can't describe it, no agent will do it well. It's the same exercise we use to implement AI in any process.

2. Mark the steps that are always done the same way

Anything that can be written as "if this happens, do that" can be solved with traditional automation. Don't spend a language model deciding something a rule already decides.

3. Identify where something has to be read or interpreted

The steps that require understanding an email, a PDF that's different every time, or a request written by a person are the ones that need AI. If there are only one or two steps like that inside a fixed path, what you need is a workflow with AI, not an agent.

4. Ask whether the path changes every time

An agent is only justified when the order of the steps or the sources to check change from case to case, and writing out every combination would be impossible. If you can draw the process as a flowchart that covers 90% of cases, you don't need an agent.

5. Define what it can do alone and what needs approval

If you decide to use an agent, write the list of actions it can take without asking (querying, summarizing, preparing drafts) and the ones that require a person's approval (sending to the customer, changing a price, recording a payment). That list is your risk control.

6. Measure against the baseline

Before switching it on, measure how much time or money the task costs today. After 30 to 60 days, compare, and add model usage and human review hours to the agent's cost. If the number doesn't improve, turn it off.

Real case: the agent that researches companies at Suricata Labs

At Suricata Labs we use our own AI agent for a task that comes up every time we start talking with a new company: getting to know it. We give it the company's name or website, the agent searches the internet for whatever it can find about it, such as its sector, its products, and its competitors, and in 5 to 10 minutes it delivers a research report.

It's a good example of when an agent is justified, because it passes the test in step 4. The path changes with every company. One has a complete website and recent news, another has barely a social media profile, and another competes in a sector you have to understand before you can look for its competitors. Nobody could write every search path in advance, so the agent decides what to look up based on what it finds along the way.

It also meets step 5. The report doesn't go to any client or make decisions for anyone; it's the team's first input. A consultant reads it, checks what matters, and digs deeper where needed. The agent prepares the work and the person closes it, which is the pattern that works best in mid-sized companies.

That same report used to take a junior consultant about 10 hours of work. Today the agent delivers it in 5 to 10 minutes, and that leaves the team time for the part that can't be automated: understanding what worries the company before the first meeting.

Not every task needs an agent. In August 2026, eighteen companies in Ciudad Juárez built order, purchase order, and inventory analyzers in three days, and none of them arrived asking about agents. They arrived with a task that cost money and a number to compare against, which is what decides the technology, not the label. Read how the workshop in Ciudad Juárez went.

Frequently asked questions about AI agents

Is ChatGPT an AI agent?

ChatGPT in normal use is an assistant, because it answers what you ask and you decide what to do with the answer. Some features of ChatGPT and similar tools do work as agents, when they receive a goal, browse, check sources, and carry out several steps on their own. The difference isn't the product but who decides the steps.

Does an AI agent replace an employee?

An agent replaces tasks, not entire jobs. In mid-sized companies, the agents that work prepare the work (searching, summarizing, drafting) and a person reviews and decides. What changes is that this person spends their time on decisions instead of copying information from one system to another.

How much does an AI agent cost?

The cost has three parts: building or licensing it, the model usage every time the agent works, and the time of the people who supervise it. The second is the one that surprises people most, because an agent can call the model dozens of times for a single task. That's why it's worth running a test with real volume before committing to an annual budget.

What is agent washing?

Agent washing is presenting a product as an agent when it isn't one, like a chatbot, an assistant, or an RPA robot with a new name. Gartner coined the term when it estimated that only about 130 of the thousands of agentic AI vendors offer real agents. The simple test is to ask whether the system decides its own steps or always follows the same path.

What are the risks of using AI agents in a business?

The main risks are that the agent takes a wrong path without anyone noticing, that it accesses information it shouldn't, and that usage costs grow more than expected. You control them by limiting the agent's permissions to the minimum, requiring human approval for actions that leave the company, and reviewing usage against results every month.

The agent is the end of the road, not the beginning

Over the next year, most mid-sized companies will get more out of simple workflows with an AI step than out of autonomous agents. That doesn't take anything away from agents. It puts them in their place: they're the tool for the variable tasks that remain once everything else has been simplified.

If you want to review a process in your company and find out whether it needs an agent, an AI workflow, or neither, at Suricata Labs we do it with your team and build the first version with them.

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Read also: Business process automation: when you need AI and when you don't