What Your 'AI Agent' Is Actually Doing: 8 Terms Explained Simply
I keep seeing 'agentic' everywhere. As something that actually runs these workflows in production, let me explain the jargon with what it really means under the hood. Agent — a loop, not a brain. The model reads the state, picks an action, runs a tool, reads the result, repeats. That loop plus a goal is the whole 'agent.' No magic. Tool / function call — the agent's only hands. It can't click or…
What Your AI Agent Is Actually Doing: 8 Terms Explained Simply
An agent is a loop, not a brain. The model examines the current state, chooses an action, performs a tool, reviews the outcome, and repeats. This loop combined with a goal forms the complete agent. There is no magic involved.
Tools or function calls serve as the agent's hands. The AI cannot click or type on its own; it must call an API. If no tool exists for a particular task, the agent cannot complete it.
Context window represents the agent's short-term memory. Everything the AI knows at any given moment must fit within this window. If it overflows, the agent loses the beginning of the task.
The ReAct loop follows a three-step process: think, act, look, and repeat. Most reasoning appears to be this pattern rather than deep thought.
MCP stands for a standard that allows tools to plug in like USB connectors. One agent can utilize many tools without requiring custom glue.
Retry and backoff mechanisms account for the time spent in real runs. Approximately one-third of the time is spent re-running actions because web pages change or websites block access.
Idempotency is the principle that performing the same action twice should not cause any issues. This distinction defines a reliable agent from one that doubles posts. Human-in-the-loop refers to the authentic version of autonomy. Real agents occasionally halt and request a code, a card, or a decision.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
