What are AI agents, and how do they use MCP?

What are AI agents, and how do they use MCP?

An AI agent is a system that uses a model to help choose and carry out steps toward a goal. Instead of producing only one text response, it may inspect information, call tools, evaluate the results, and decide what to do next.

The exact meaning of agent varies across products. A useful practical distinction is whether the model helps select actions within a workflow.

The action-and-observation loop

Imagine an assistant asked to identify unresolved support tickets about a broken login screen. It might search a ticket system, inspect matching tickets, group similar reports, and produce a summary with links.

Each tool result becomes new context. The system can then decide whether it has enough evidence or needs another search. Limits on permissions, execution time, and available tools shape what it can actually do.

Where MCP fits

The Model Context Protocol, or MCP, defines a common way for AI applications to connect to external tools and information sources. An MCP server can expose capabilities that a compatible application discovers and uses.

MCP is an integration protocol. It is not a language model, a reasoning algorithm, or a requirement for building an agent. An application can also use ordinary APIs or other integration methods.

Connections need application-level decisions

Connecting a tool does not automatically make every action appropriate. The application must decide what data the user can access, which actions are permitted, and when confirmation is needed.

For example, reading ticket details and deleting tickets have different consequences. A useful agent design treats those capabilities separately and verifies that tool results support its final answer.