AI Agent

An AI agent is built on a large language model but goes beyond text generation. It has access to tools like APIs, databases, and file systems. When given a task, it decides which tools to use, in what order, and how to interpret the results.

The basic architecture has four parts: a perception layer (input from users or systems), a reasoning engine (usually an LLM), a tool layer (APIs and integrations), and a memory layer (conversation history and retrieved context). These parts work together so the agent can handle tasks that require multiple steps and decisions.

In practice, enterprises use AI agents for things like customer support (pulling order data, checking policies, issuing refunds), internal operations (processing invoices, routing approvals), and research (gathering information from multiple sources and synthesizing it into a report).

What separates an agent from a chatbot is the ability to take action. A chatbot answers questions. An agent answers questions and then does something about it. It can update a CRM record, trigger a workflow, or escalate to a human when it hits a boundary it cannot handle.

Most production AI agents today run with guardrails that limit what actions they can take and when they need human approval. The goal is reliability first, autonomy second.