What Is an AI Agent? How They Work and What They Can Actually Do

An AI agent is software that uses a large language model to work toward a goal over many steps instead of producing a single reply. Given a task, it decides what to do next and calls a tool. The tool might be a search engine, a code runner or a business application. The agent reads the result and keeps going until the job is done or something stops it. A chatbot answers a question. An agent carries out a task.

What is an AI agent, and how does agentic AI differ from generative AI?

Anthropic's engineering guidance separates agents from workflows. In a workflow, it says, developers fix the sequence of steps in code. An agent, it says, lets the model direct its own process and choose its own tools.

Generative AI is the underlying ability to produce text, images or code on request. Agentic AI puts that ability in charge of actions. An MIT Sloan explainer published in February 2026 draws the line this way. A chatbot responds to what it is asked. An agent, it says, plugs into other software and finishes tasks with little or no human oversight.

How do AI agents work?

Most agents run the same loop: plan a step, act, observe what happened, then plan again. Four parts make that possible.

• A model. The language model does the reasoning and picks the next action.
• Tools. These are functions the model can call, such as web search, a database query or a code interpreter.
• Memory and retrieval. Agents keep notes on their progress and fetch relevant documents as they go.
• Guardrails. Sandboxes (isolated environments), permission prompts and stopping conditions limit what an agent may do without a person signing off.

Some agents go beyond structured tool calls and operate a computer the way a person would. Anthropic's documentation describes a computer use tool in which the model studies screenshots and returns mouse clicks and keystrokes for the developer's application to carry out. Google documents a similar capability for its Gemini models. Google warns that, as a preview feature, it can make mistakes and carry security flaws.

What is the Model Context Protocol?

The Model Context Protocol, or MCP, is an open standard that Anthropic introduced in November 2024. Its purpose is to let an AI application connect to a data source or tool without a custom integration for every pairing. The project's documentation compares it to a USB-C port for AI applications.

Under the specification, an MCP server offers three kinds of things to an AI application:

• tools the model can execute
• resources that supply data and context
• reusable prompts

In December 2025 the Linux Foundation announced the Agentic AI Foundation. MCP was contributed to the new foundation by Anthropic, alongside Block's goose agent framework and OpenAI's AGENTS.md. AGENTS.md is a format for giving coding agents project instructions. The foundation said more than 10,000 MCP servers had been published. It said Claude, ChatGPT, Gemini, Microsoft Copilot, Cursor and VS Code had adopted the protocol. The current revision, dated July 28, 2026, dropped protocol-level sessions. Its maintainers say that lets servers run behind an ordinary load balancer.

What can AI agents actually do today?

Software development is the best-documented use. Anthropic's documentation describes Claude Code as an agentic coding tool that reads a codebase, edits files and runs commands. Anthropic analyzed agent activity on its public API in a study published in February 2026. In that study, software engineering accounted for nearly half of all tool calls. That is one provider's data, not an industry-wide measure.

Stanford's 2026 AI Index covers AI performance in 2025. OSWorld is a benchmark of computer tasks spanning several operating systems. The report says agents' accuracy on it climbed from about 12% to 66.3%. That left them within 6 percentage points of human performance, it says. The same report notes that agents still fail about one in three attempts on structured benchmarks.

The evaluation group METR estimates how long a task, measured in human expert time, an agent can complete with 50% reliability. Its results page was last updated on May 8, 2026. The highest estimate there was about 17 hours, for an early version of Anthropic's Claude Mythos Preview. METR cautions that measurements above 16 hours are unreliable with its current tasks. In a January 2026 analysis, METR put the doubling time at around seven months for 2019 to 2025. It put the figure at roughly four months for models released since 2023. Its tasks come mainly from software engineering, machine learning and cybersecurity. METR says a given time horizon does not mean a model can handle all work of that length.

The bottom line

An AI agent is a language model placed in a loop with tools, memory and limits. As of October 2026, the published evidence is strongest for coding. Benchmark scores for operating ordinary software have risen quickly. But failure rates are still high enough that the limits matter. Anthropic's computer use documentation advises running agents in a dedicated virtual machine or container. It also advises having people confirm consequential actions. The MCP specification says host applications must obtain explicit user consent before invoking any tool, though the protocol itself cannot enforce that.