AI Agent
An AI system that can take actions on its own, using tools and making decisions across multiple steps to accomplish a goal.
What It Is
An AI agent is a language model that goes beyond generating text by actually taking actions in the world. While a basic chatbot responds to your message and waits for the next one, an agent can decide what steps to take, use external tools (search the web, read files, call APIs, write code), evaluate the results, and continue working toward a goal across multiple steps. The agent uses the language model as its “brain” for reasoning and decision-making, but it also has “hands” in the form of tool access. Claude Code is an AI agent: it reads your project, decides what to change, makes edits, and verifies its work.
Why It Matters
Agents are the next evolution of how people interact with AI. Instead of going back and forth in a chat, you give the agent a goal and it figures out how to achieve it. This is what makes AI genuinely useful for complex work like building software, managing data pipelines, or running multi-step research tasks. For operators, understanding what agents can and cannot do helps you design workflows that leverage their strengths (multi-step execution, tool use) while compensating for their weaknesses (they can go off track without clear instructions and guardrails).
In Practice
An AI agent workflow might look like this: you tell the agent “research the top 5 competitors in this space and create a comparison table.” The agent searches the web, reads multiple pages, extracts relevant data, organizes it into a table, and presents the result. In n8n, agent nodes can be configured with specific tools and instructions to handle multi-step tasks within your automation pipeline.