An AI agent is a system built around a language model that can plan a sequence of steps, call tools or APIs to act on the world, check its own results, and keep going until a goal is met or a person steps in.
An AI agent is not the model. It is the loop of planning, acting, and checking that the model runs inside.
AI agent vs agentic AI: what is the difference?
They are related, not identical. Agentic AI is the approach: build software that plans and acts with minimal supervision. An AI agent is one running instance of that approach, built for one job, like triaging tickets.
What does an AI agent actually need to run safely?
Beyond the model, it needs tools it can call, standardized through something like MCP, and a way to know when it is done or stuck, plus a human approval gate on any action that touches money, external messages, or production data. When it needs facts from company data rather than what the model already knows, and decides dynamically what to query, that is agentic RAG.
Frequently asked questions
What is the difference between an AI agent and agentic AI?
Agentic AI is the general approach: software that plans and acts with minimal supervision. An AI agent is one specific implementation of that approach, built to do one job, like triaging tickets or drafting reports.
What tools does an AI agent typically use?
Whatever the task needs: internal APIs, a database, a search index, a file system, or another agent. Many agents reach those tools through MCP, an open protocol that standardizes how a model calls external tools and data.