What AI Agents Actually Are, and Where the Word Ends
Gartner went looking for AI agents in 2025 and found something worth remembering. Of the thousands of vendors claiming agentic capability, roughly 130 were building anything that deserved the word. Not 130 good ones out of a field of 200. One hundred and thirty out of thousands. That ratio is the reason this question is hard to answer by reading marketing pages. The term is applied to chatbots,…
Gartner identified roughly 130 AI agents among thousands of vendors in 2025. This distinction is crucial because the term "agent" is applied loosely, causing confusion. An AI agent is a system where the model decides the next action, unlike a workflow where the code determines the sequence. This distinction has significant consequences.
In a workflow, the developer decides the steps, while in an agent, the model selects the actions based on the goal and capabilities. This leads to a dynamic, non-scripted process that creates different paths even for the same task. The key components of an AI agent are the model, tools, memory, and loop, all working together under guardrails to ensure responsible operation.
These components are essential for reliability, as errors can compound in a chain of probabilistic decisions. Examples like coding assistants, research assistants, support systems, and operations watchers demonstrate branching decision-making that sets agents apart from traditional workflows. However, not all useful systems qualify as agents; chatbots retrieving information from documents are not agents despite their capability.
Understanding the technical definition of an AI agent helps clarify its unique characteristics and operational challenges.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written; read the original for the full account.
