AI Agent (Agentic AI)

An AI agent is software built on top of a language model that doesn't just generate text but takes steps on its own to reach a goal. The difference from an ordinary chatbot is autonomy. A chatbot answers a question. An agent is given a task ('find a free slot and book the meeting'), breaks it into smaller steps, uses tools (calendar, email, search), evaluates the result, and continues until the task is done or it hits a boundary.

A typical agent runs in a loop: the model decides what action to take, the action is executed (call an API, read a file, run code), the result is fed back to the model, and it decides on the next step. This pattern is often called a reasoning-action loop. To work reliably, an agent needs access to tools (today often through the Model Context Protocol), some memory, and clearly defined limits on what it may and may not do.

A few practical notes from our experience. Agents are strong on tasks with a clear brief and a verifiable result - data processing, research, routine operations. They struggle where the brief is vague or where a mistake breaks something. An agent allowed to change data or move money needs guardrails, logging, and often a human confirmation step for irreversible actions. Without those, you risk an autonomous system doing the wrong thing quickly and confidently.

For businesses, the realistic approach is to start with a narrowly scoped task whose result is easy to check, then gradually expand what you trust the agent with.