AI Agent
An AI agent is a system that takes a goal, breaks it into steps, and executes those steps autonomously—calling tools, reading results, adjusting course—without a human approving each action. Where a chatbot waits for your next prompt, an agent decides what to do next on its own. The concept borrows from reinforcement learning and robotics, but the current wave runs on large language models that can reason about which tool to use when. Agents are powerful when the task has clear success criteria and bounded risk. They are dangerous when the goal is vague, the environment is unfamiliar, or the cost of a wrong action is high. Most production agent systems today still need tight guardrails and human checkpoints—full autonomy remains more demo than reality.
Related terms:
Context Window
A context window is the maximum amount of text a language model can process in a single call—input and output combined—measured in tokens.
AI Evaluation
AI evaluation is the practice of systematically measuring an AI system’s performance against defined criteria—accuracy, latency, cost, safety, and user...
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates.