What is an AI agent, and what does it actually do?

What is an AI agent?

An AI agent is a system that completes a task for you with some independence: you give it a goal, and it works out the steps. OpenAI’s guide for product teams puts it in one line: “Agents are systems that independently accomplish tasks on your behalf.”

Anthropic, which makes the Claude models, draws the line at control. In its engineering guide, agents are “systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks”. A workflow, by contrast, follows code paths written in advance.

Two words carry these definitions: task and tools. An agent is judged on the work it completes. And it acts through tools: an API, a database, a mailbox, a screen in your ERP. Without tools, a language model can only produce text. With them, it can look up an order, update a record or send a reply.

That also tells you what an agent is not. OpenAI’s guide lists “simple chatbots, single-turn LLMs, or sentiment classifiers” as applications that use a language model without letting it run the work. They can be very useful, and they stay on the other side of the line.

How an AI agent works

An agent runs a loop. It looks at the situation, picks an action, uses a tool, reads the result and decides again, until the task is done or it hands over to a person. Anthropic describes the agents it sees in production as “typically just LLMs using tools based on environmental feedback in a loop”. The loop itself is simple, so the quality comes from the tools, data and instructions you give it.

OpenAI’s guide breaks an agent into three components:

• The model: the language model that reasons and decides.
• The tools: the functions or APIs it can call to fetch information or take action.
• The instructions: the rules and guardrails that define how it behaves.

Google Cloud adds memory: short-term memory for the exchange in progress, long-term memory for history. In a company, that memory is often the business record itself, such as the ticket, the order or the customer file.

Here is an illustration of a typical case, not a client project. A supplier emails to say a delivery will be late. A rule-based script needs the email to follow a known format; the next supplier writes it differently and the script fails. An agent reads the message as written, finds the purchase order in the ERP, checks which customer orders depend on it, drafts a note to each customer affected and a summary for the buyer. Then it stops and asks the buyer to approve before anything goes out. Every lookup is a tool call. The pause at the end is an instruction.

Connecting agents to your systems has become more standard. In November 2024, Anthropic open-sourced the Model Context Protocol (MCP), a common way to link models with business tools and data sources. In December 2025 it donated MCP to the Agentic AI Foundation, a fund hosted by the Linux Foundation, and reported more than 10,000 active public MCP servers at the time. For a buyer, the point is practical: an agent built today can reach many of your tools through a shared standard, which cuts the connector work for each system.

AI agent, assistant, bot or workflow: where the line falls

The real difference is who decides the next step. In a bot, rules decide. In an assistant, you decide. In a workflow, the developer decided in advance. In an agent, the model decides, within limits you set. Google Cloud draws the same split between agents, assistants and bots; Anthropic adds the workflow, which is where many good business projects actually land.

Most systems that work in production mix the last two rows. The model decides where cases vary, and fixed, testable code handles the rest. Anthropic’s advice to developers goes the same way: it recommends “finding the simplest solution possible, and only increasing complexity when needed”. If a workflow does the job, it costs less to run and is easier to audit than an agent.

Our document extraction demo is a workflow of that kind. Try it on a sample invoice to see how far fixed steps go before you need an agent at all.

The main types of AI agents

Agents are usually sorted by how they meet people and by how many work together. Google Cloud separates interactive agents, which talk with a user in customer service or internal help, from background agents that automate routine tasks with little or no human input. It also separates single-agent systems from multi-agent systems, where several agents split a job between them.

By function, Google Cloud groups the agents companies deploy into six families: customer agents, employee agents, creative agents, data agents, code agents and security agents. For an operations team, most first projects sit in the first two families: answering and routing requests, and taking over back-office tasks such as order intake or invoice matching. Our page on AI agents for business lists the tasks we see most often.

On the number of agents, the model makers agree. OpenAI’s guide recommends to “maximize a single agent’s capabilities first” and to split the work only when one agent can no longer cope. Every extra agent adds a hand-off, and every hand-off is a place where context can be lost.

Where an AI agent pays off, and where it does not

An agent is worth building when the work needs judgment on messy input and scripts keep breaking. OpenAI’s guide gives three signs: complex decisions with many exceptions, rule sets that have become too costly to maintain, and heavy reliance on unstructured data such as emails, documents or free text. When a use case does not clearly meet these criteria, the same guide concludes that “a deterministic solution may suffice”.

Analysts are blunt about the gap between promise and delivery. In June 2025, Gartner predicted 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”. The same release says many use cases presented as agentic “don’t require agentic implementations”. Gartner also estimates that only about 130 of the thousands of vendors selling agentic AI are real, and calls the rest “agent washing”.

Our reading: the projects that last start from a task with a measurable result, such as hours returned to the team, tickets closed or errors avoided. That is why we define each project by what runs in production at the end and by the indicator that proves it.

Agents also have plain limits. Google Cloud lists tasks that need deep empathy, situations with high ethical stakes, and unpredictable physical environments. Add any action that cannot be undone or checked afterwards. Those stay with people, or behind a human approval.

Risks and rules: what changes when software can act

Once software can act, its mistakes become actions. The OWASP Top 10 for LLM applications (2025 edition) calls this risk “Excessive Agency”: damaging actions triggered by “unexpected, ambiguous or manipulated outputs” from the model. OWASP traces it to three causes (too many functions, too many permissions, too much autonomy) and recommends keeping tools and permissions to the minimum and requiring a person to approve high-impact actions.

The second risk is manipulation. OWASP ranks prompt injection first in the same list and notes that the attack can arrive through external content “such as websites or files”. An agent that reads incoming emails reads text written by strangers. The safeguards are design choices: separate what the agent reads from what it may do, keep its rights narrow, and log every action it takes.

In Europe, two texts frame the rest.

• The AI Act covers any “AI system”, defined in Article 3 as a machine-based system “designed to operate with varying levels of autonomy”. Its transparency rules (Article 50) require that people be told they are interacting with an AI system unless it is obvious; the European Commission states that these rules apply from August 2026. Obligations for high-risk uses listed in Annex III, such as recruitment or credit scoring, now apply from 2 December 2027, a date set by the Digital Omnibus on AI published in July 2026. The text keeps changing, so check it before relying on a date. Our guide to EU AI Act compliance follows those changes.
• The GDPR gives people “the right not to be subject to a decision based solely on automated processing” when the decision has legal or similarly significant effects (Article 22). Exceptions exist, but where they rely on a contract or consent, the company must still offer at least human intervention and a way to contest. An agent that approves a refund, a loan or a job application needs a person in that loop.

If your data must stay in Europe, the choice of model and hosting belongs in the design from day one. Our page on private LLMs hosted in Europe explains the options.

How to start with a first agent

Start with one task, one indicator and one person who owns the result. A good first candidate is frequent, messy enough to defeat a script, and costly when it is done late: order intake, ticket triage, supplier follow-up. Before anyone writes code, collect real past examples and write down what a correct outcome looks like for each. Those examples become the test set the agent must pass.

Keep the first version narrow. Give read-only access where you can, put a human approval before anything leaves the company, and keep a log a manager can read. Widen the agent’s rights only as the measured error rate allows.

Three next steps, depending on where you stand:

• To understand the wider shift, read our guide to agentic AI.
• To weigh no-code tools, frameworks and custom builds, read how to build an AI agent.
• To find where an agent would pay off first across your company, an AI readiness assessment ranks the candidate tasks. If the agent has to work inside your CRM or ERP, see our AI integration services.

Or start with the task you already have in mind. Book your free 30-minute assessment: tell us which work eats your team’s week, and we tell you honestly whether an agent can take it over, how it would be supervised and what a first version needs. We reply within one business day, and a first agent usually reaches production in four to eight weeks, depending on the systems involved.