What Are AI Agents? Definitions, Types, and Cutting‑Edge Technologies Explained

1. Definition and Classification of AI Agents

An AI agent (Agent) is a system that perceives its environment, makes decisions, and takes actions to achieve specific goals, typically possessing memory, planning, tool use, and autonomous behavior.

1.1 What Is an Agent

Agents differ from traditional AI by being continuous learners that can adapt and optimize their behavior in complex settings.

1.2 OS Agent: Operating System Agent

OS agents interact with graphical user interfaces (GUI) of computers and mobile devices to perform tasks.

Environment: Windows, macOS, Android, etc.

Observation space: screen captures, DOM structures, etc.

Action space: clicks, inputs, swipes, etc.

1.3 Main Categories of Agents

Language‑based agents : use only textual descriptions (HTML/XML) as input.

Vision‑based agents : rely solely on screen screenshots.

Vision‑language mixed agents : combine screenshots with textual descriptions.

Vision‑based (e.g., SpiritSight) and vision‑language mixed agents (e.g., MobileFlow) are current research hotspots due to cross‑platform compatibility and rich perception.

2. Core Capabilities of AI Agents

2.1 Understanding

Agents must interpret user instructions and task goals. Recent work such as MobileFlow introduces GUI Chain‑of‑Thought (CoT) to enable reasoning similar to humans.

2.2 Perception and Localization

SpiritSight’s Universal Block Parsing (UBP) resolves ambiguities in high‑resolution dynamic inputs.

MobileFlow’s hybrid visual encoder supports variable‑resolution inputs, improving detail perception.

OpenAI ComputerUse employs a closed‑loop vision‑operating‑system pipeline to analyze the entire screen and execute precise actions.

2.3 Planning

Global planning: generate a complete action sequence before execution.

Iterative planning: adjust the plan dynamically based on environmental feedback.

MobileFlow adopts a four‑step iterative framework (observe → reason → act → summarize).

2.4 Action

Mouse/touch actions: click, long‑press, drag.

Keyboard actions: text entry, shortcuts.

Navigation actions: scroll, page flip, tab switching.

3. State‑of‑the‑Art Technologies

3.1 OpenAI ComputerUse

Principle: based on the Computer‑Using Agent (CUA) model, leveraging GPT‑4o’s visual and reasoning abilities.

Workflow: instruction understanding → action generation → execution & feedback → state understanding → iterative improvement.

Supported environments: browsers, macOS, Windows, Ubuntu (mobile platforms not yet supported).

Applications: automated testing, exploratory testing, regression testing, cross‑platform consistency testing.

3.2 SpiritSight: Vision‑Driven GUI Agent

Innovation: introduces the large‑scale multi‑level GUI dataset GUI‑Lasagne and the Universal Block Parsing method.

Features: end‑to‑end pure visual perception, no reliance on HTML/XML.

Performance: surpasses existing methods on benchmarks such as Multimodal‑Mind2Web.

Cross‑language ability: fine‑tuning on a small target‑language dataset enables operation in languages like Chinese.

3.3 MobileFlow: Mobile‑Focused Agent

Model architecture: built on Qwen‑VL‑Chat with a hybrid visual encoder, supporting up to 21 B parameters.

Technical traits: variable‑resolution input, strong multilingual support, mixture‑of‑experts (MoE) design.

Training strategy: GUI alignment tasks (localization, reference, QA, description) combined with GUI Chain‑of‑Thought.

Deployments: successfully used in software testing and ad‑preview review scenarios.

The article synthesizes recent research on AI agents, outlines their taxonomy, essential abilities, and highlights leading implementations that push the frontier of autonomous GUI interaction.