This post originally appeared on tokenjam.dev/blog . It's part of a 14-post series on the agentic AI ecosystem.

TL;DR

An AI agent uses an LLM to reason about a goal and decide what actions to take, calling tools and observing results until the goal is reached

Agents differ fundamentally from chatbots (which don't act) and workflows (which don't decide)

The ReAct pattern (reasoning + acting) is the dominant architecture in modern agent systems

Agents range from copilots that suggest actions to fully autonomous systems that run unattended for hours

Key components: the LLM (reasoning), tools (actions), context/memory (state), and a control loop (orchestration)

What is an AI agent? An AI agent is a system that uses a large language model to make decisions and take actions in pursuit of a goal. It calls tools, observes what they return, and iterates until the goal is reached.…