Why 2025 Is the Year of AI Agents: Definitions, Types, and Real‑World Examples

Difference between AI Agent and Large Models

Large models such as DeepSeek or Qwen accept a user query and generate a response directly. Example: a prompt to write copy yields text in a single step.

An AI Agent follows a multi‑step pipeline that mirrors human writing:

Generate an outline with a model.

Retrieve relevant information based on the outline.

Summarize the information and produce draft content.

Evaluate the draft using the same or different models and iteratively refine it.

Output the final content once quality criteria are met.

This orchestrated process typically yields higher‑quality results.

What is an AI Agent?

An AI Agent is a system that incorporates one or more large models, adds orchestration logic, and behaves more like human cognition. It treats a large model as a processing component within a larger workflow.

AI Agent Classifications

Reflection System

Definition: A self‑reflection mechanism that continuously improves output quality by mimicking human thinking and correction.

Example: Grammarly uses language models to generate text while constantly checking spelling, grammar, and punctuation, and incorporates suggestions from multiple models to refine the final output.

Tool Use System

Definition: Enables a large language model to call external tools (calculator, database, search engine, etc.) during answer generation to handle tasks it cannot solve directly.

Example: The VS Code plugin Cline+Continue acts as an AI Agent for coding: it creates files, writes code, checks for errors, and runs debugging sessions by invoking appropriate tools, extending DeepSeek’s capabilities to the programming domain.

Planning System

Definition: The ability of a model to devise a step‑by‑step plan for solving complex problems, breaking them into manageable sub‑tasks.

Example: Image Describer X processes an image of a girl reading by:

Calling an OpenPose model to extract the pose.

Using Google ViT to convert the pose into an image.

Applying ViT‑GPT2 to turn the image into descriptive text.

Employing FastSpeech to synthesize speech from the text.

Multi‑Agent System

Definition: The most complex architecture, where multiple agents collaborate under a shared goal, dividing tasks and interacting to complete a project.

Example: ChatDev (2023) builds a virtual software company. After a user specifies a software requirement, distinct agents handle product design, UI design, code implementation, and testing, producing source code, dependency files, and user manuals. The project is open‑source at https://github.com/OpenBMB/ChatDev.

Industry Exploration

Tencent’s “Yuanqi” platform provides an open agent mode where developers can add plugins (tool use) and planning workflows, enabling agents such as PPT assistants and parenting assistants.

Google released “Astra” in May 2024, an AGI‑style system that leverages a phone camera to perceive visual and auditory input, allowing users to click, draw or code and receive explanations, illustrating a multi‑agent application.

Conclusion

The development trajectory can be viewed as three successive stages: AI → AI Agent → AGI . Powerful large models such as DeepSeek and Qwen provide the foundation for building agents, and the emergence of AI Agents brings the vision of general artificial intelligence closer to reality.