What is an AI agent? How agents work, tools and risks

An AI agent is a software system that works toward a goal with some independence: it plans the steps, uses tools such as web search, code execution or a browser, checks the results and keeps going until the task is done. A chatbot answers your question and leaves the next move to you. An agent tries to make that move itself.

This guide explains how agents work, where the line with chatbots sits, which products exist and what can go wrong. The field moves fast, so treat product names, prices and availability as things to confirm on each provider's own site.

What is an AI agent, in plain terms?

The idea is older than today's chatbots. In Russell and Norvig's classic AI textbook, an agent is anything that perceives its environment and acts on it. Modern AI agents apply that idea with a large language model (LLM) at the centre.

In a typical setup the model acts as the planner. It breaks a task into steps, picks a tool, reads what the tool returns and decides what to do next, looping until it finishes. Memory and state wrap around that loop so the agent can keep track of what it has already done.

How is an AI agent different from a chatbot?

The short answer is action. A chatbot responds to one prompt at a time, and a person decides what to do with the answer. An agent takes on a multi-step job, such as searching a site, filling in a form, running code or editing a file, and performs those steps itself.

In practice the boundary is blurry. "Agent" and "agentic" have become marketing words without an agreed definition, and Gartner uses the term "agent-washing" for vendors that relabel ordinary chatbots as agents. The useful test is simple: what can the product actually do on its own, beyond producing text?

How do AI agents use tools?

Agents reach the outside world through tool calling, also called function calling. The model states that it wants to use an API, run a search, execute code, open a browser or operate a computer interface; the surrounding software carries out the request and hands the result back to the model.

Standards are emerging to make those connections portable. Anthropic introduced the Model Context Protocol (MCP) in November 2024 as a common way for models to plug into tools and data sources, and OpenAI, Google and Microsoft later adopted it. Google also announced an Agent2Agent (A2A) protocol in 2025 for agents to communicate with each other; how A2A is governed today is worth checking.

How autonomous are AI agents?

Autonomy is a spectrum. At one end, the agent asks a human to approve every step. At the other, it runs a whole task unattended. Most real deployments sit somewhere in between, with checkpoints for sensitive actions.

Anthropic offers a helpful distinction. "Workflows" follow fixed, pre-written code paths, while "agents" let the model choose its own steps and tools. Workflows are easier to predict and test; agents are more flexible but harder to control. Many teams start with a workflow and add agent-style freedom only where it pays off.

Which AI agent products exist?

Most major AI companies now ship agents. OpenAI launched Operator, a web-browsing agent, in January 2025 and folded it into "ChatGPT agent" in July 2025. It also offers Codex, a coding agent, and an Agents SDK for developers.

Anthropic released "computer use" in October 2024, letting Claude operate a desktop interface, and also offers Claude Code and the Claude Agent SDK. Google has Project Mariner, agent features in Gemini and the coding agent Jules. On the enterprise side there are Microsoft Copilot Studio agents and Salesforce Agentforce, and other frequently cited examples include Manus and Perplexity's Comet browser.

A caution: names, pricing and country availability change often. ChatGPT agent, for instance, was initially limited to paid plans, but current terms should be checked directly with OpenAI rather than taken from older articles.

What are the risks of AI agents?

The best-known threat is prompt injection, where malicious instructions hidden in a web page, email or file hijack the agent. OWASP ranks it first in its Top 10 for LLM applications and has published separate guidance on agentic AI.

Other risks include actions that cannot be undone, such as payments, deletions or sent messages; data leaking to the wrong place; agents given more permissions than they need; small errors and hallucinations compounding over many steps; unexpected costs; and unclear liability when something goes wrong.

Expectations in business are cautious too. In June 2025 Gartner predicted that more than 40% of agentic AI projects would be cancelled by the end of 2027. Sensible practice is to grant minimal permissions, require human approval for consequential steps and keep agents away from sensitive accounts until you trust them.

Regulation is still catching up. The EU AI Act's obligations are phasing in between 2025 and 2027, though parts of the timeline have been disputed or delayed, and NIST's AI Risk Management Framework is the main US reference. In Türkiye, draft AI legislation has been discussed, but its current status should be checked with official sources.