Agentic AI

Overview

Agentic AI marks a shift from models that answer questions to systems that accomplish tasks. Where a conventional language model responds once and stops, an agent breaks a goal into steps, decides what to do next, calls external tools or data sources, observes the result, and continues until the objective is met. It is the difference between a model that tells you how to do something and one that goes and does it.

How it works

An agent wraps a language model in a control loop. The model reasons about the current state, chooses an action, perhaps querying a database, calling an API, or running code, and the result of that action is fed back in as new context for the next decision. This cycle repeats, often many times for a single task, with the model effectively planning and self-correcting as it goes.

Why it matters

Because an agent invokes its underlying model repeatedly rather than once, agentic workloads consume far more inference compute than simple question-and-answer use. A single user request can trigger dozens of model calls behind the scenes. This multiplies the demand on inference infrastructure and makes throughput, latency, and cost per token decisive, precisely the pressures current-generation hardware is designed to absorb.