AI giants are making heavy investment in the field. A in - depth analysis of AI agents: Why is it considered the ultimate form of AI?

Today, I came across an opinion stating that few emerging technologies can offer more opportunities for organizations to accelerate productivity and transform business operations than Agentic AI. Its prospects even surpass those of its cousin, Generative AI (GenAI).

Additionally, I read a report from Huatai Securities, claiming that generative AI is entering a new development stage dominated by AI agents.

The Agentic AI mentioned here is actually the common concept of an AI agent. Recently, I've participated in many events and tested several AI products featuring agents. I've clearly felt the continuous heating up of the agent concept.

I can clearly sense that this might be the latest trend in the long - agitated AI field since the emergence of ChatGPT. Today, I'll try to take you on a journey to explore this broader global landscape of AI agent development.

1: From "Knowledgeable Brain" to "All - around Worker": What Exactly is an AI Agent?

To understand why AI agents are highly anticipated, we first need to clarify the fundamental differences between them and the familiar Generative AI (GenAI).

If GenAI, represented by ChatGPT, is a "brain" that is knowledgeable and answers every question, then an AI agent is like giving this brain "hands and feet", transforming it from a "conversationalist" into an "actor".

GenAI tools are constrained by their programming logic and are good at generating content based on instructions, but their ability to take action stops there. In contrast, an AI agent is endowed with more advanced capabilities:

It is entrusted with a goal and can then independently understand, plan, call tools, and interact with the environment to achieve this goal.

For example, in a test I conducted before, an AI agent could generate a high - definition video blockbuster lasting three to five minutes, or even ten minutes, with just one sentence. Tasks such as scriptwriting, storyboarding, background music selection, and image generation, which would take a human team weeks to complete, can be done by an AI agent in one go.

Industry experts have proposed a clear evolutionary path for AI agents, which can be roughly divided into several stages: from the L1 - level chat assistant that can only perform simple Q&A at the beginning, to the L2 - level workflow agent that requires human - preset processes, and then to the L3 - level reasoning agent that can independently plan tasks like a domain expert. Currently, the most competitive area is the L4 - level multi - agent system, which enables multiple agents with different specializations to collaborate and solve complex cross - domain problems like a team.

From this evolutionary path, we can see that the development direction of AI is shifting from pursuing a single "bigger and stronger" model to building a "smart ecosystem" capable of collaborative operations.

This is the fundamental reason for the continuous popularity of the AI agent concept - it marks the transformation of AI from a "tool" to a true "partner" and "digital workforce".

2: Global Giants "Draw Their Swords": The "Present Continuous Tense" of the AI Agent Race

The wave of AI agents is not just empty talk. Looking globally, tech giants have long been heavily invested, vying to showcase their "aces" and accelerating this future concept into the "present continuous tense".

Microsoft: Integrating Agents into Every Corner of Productivity

Microsoft's strategy is to have "Copilot everywhere". It is committed to upgrading Copilot from an in - app assistant to a "super agent" that can span the Windows operating system, the entire Office 365 suite, the Teams collaboration platform, and Azure cloud services.

In the future, Copilot will not only help you write emails or summarize documents. It will be able to understand complex instructions like "Prepare a complete report for next week's sales meeting", then independently retrieve data from Excel, generate charts in PowerPoint, extract key points from Teams chat records, and finally integrate them into a complete presentation for you.

In addition, Microsoft has open - sourced a framework like AutoGen, aiming to help developers build powerful multi - agent applications. Its goal is to create a large - scale, collaborative AI agent network and deeply integrate agent capabilities into every aspect of digital work.

Google: Defining Future Interaction with Multimodal General AI

Google is betting on multimodality and generality. The Project Astra plan, which made a stunning debut at its I/O conference, is a prime example.

The goal of Astra is to create a general AI agent that can see, hear, speak, remember, and understand complex situations. In the demonstration, it could recognize the surrounding environment in real - time through the phone camera, understand code, and even remember the storage locations of items, demonstrating its great potential as an "all - around assistant for daily life".

Behind this is the powerful ability of Google's Gemini model, especially its innate multimodal understanding and "tool use" ability, which allows it to call various APIs to perform real - world tasks.

For enterprise users, Google provides the Vertex AI Agent Builder to help them quickly build agents for specific business scenarios.

OpenAI: A Key Milestone on the Road to AGI

As the pioneer leading the current AI wave, OpenAI sees agents as the key path to achieving Artificial General Intelligence (AGI). The GPTs it launched can be regarded as an initial attempt to build agents, allowing users to create customized versions of ChatGPT for specific tasks.

However, OpenAI's ambition goes far beyond this. It is actively researching the next - generation agents that can independently operate the computer desktop environment, use browsers, and operate various software to complete complex tasks. Such agents will be able to interact with the digital world like humans, from booking flights to managing complex projects, truly becoming an extension of human capabilities.

NVIDIA: Providing an "Arsenal" for the Agent Era

In this race, NVIDIA plays an indispensable role as an "arms dealer". It not only provides powerful GPUs for global AI companies but, more importantly, is building a complete platform for agent development and operation.

Tools such as NIM (NVIDIA Inference Microservices) it launched allow developers to easily package models into callable services, which is the cornerstone of building agents.

Recently, NVIDIA even released the "GR00T" project designed specifically for humanoid robots, demonstrating its ambition to extend agent capabilities from the digital world to the physical world.

Of course, in this global race, China's tech forces should not be underestimated either. Companies such as Baidu and 360 have also launched multi - agent platforms for the public that can handle complex tasks, indicating the global synchronous development trend in this field.

3: "Digital Employees" Become a Reality: How Agents Will Revolutionize All Industries

Having talked about all these high - end technologies, how exactly will these "AI agents" change our work and lives? Simply put, all industries will welcome a group of tireless and super - capable "digital employees".

For example, we're all fed up with dealing with robot customer service that only says "How can I help you?" Future agent - based customer service will be different. They will have more autonomy, be able to retrieve your information like a real person, understand your problems, and truly solve them for you.

Within a company, these "digital employees" will really shine. An agent in charge of the warehouse can monitor inventory 24/7. Once it detects a shortage, it can independently rearrange the delivery route and time.

For programmers, many tedious and repetitive programming tasks can be handed over to AI agents. They can help write new functions, check code, and even catch bugs in real - time. Even in some cool fields like "digital twin" (creating an identical digital model of a real machine in the computer), agents can analyze various data, simulate machine operation, tell you in advance where a malfunction will occur, and even collaborate to arrange repairs.

Of course, there are both benefits and risks. The most direct challenge is network security. Imagine if hackers also use "agent - based hackers", they can launch fast and powerful automated attacks. This forces us to have our own "security agent" teams. In the future, the offense and defense in the network world may very well be a battle between two groups of AI agents.

Does it sound like the future is here but also a bit far away? Indeed, although the future looks bright, there are still several hurdles to overcome.

The biggest problem is that currently, the agents developed by different companies can't really "speak the same language". They lack unified standards and interfaces, making it difficult to cooperate smoothly across platforms and companies. Once this problem is solved, the capabilities of agents will be "omnipotent".

4: A Long but Promising Road: Challenges and Future Outlook

So, we are now at a very critical starting stage. Although the videos of all - powerful AI assistants look as magical as magic, it will take a lot of effort to make them truly popular.

So what should we do? Experts' advice is very practical:

Start cautiously, but start now. Each of us and every company should actively understand and explore what these AI agents can do for us, especially find practical uses that can bring real returns. You can start with some small pilot projects, give your AI agent a "key", let it start running in the digital world, and accumulate experience.

Going back to the initial question: Is the AI agent the latest trend in AI evolution? The answer is yes. It marks the evolution of AI from a passive "content generator" to an active "task executor". This is a fundamental leap.

Now is the best time for us to explore AI agents. We need to learn from existing successful cases, start small, begin to build and pilot, and give agents the "key to digital practice".

Only by personally exploring can we truly understand its potential and boundaries, lead your personal life and organizational development, successfully cross the learning curve, and move from the ideal to success.

This article is from the WeChat official account "New Mango xAI", author: Green. Republished by 36Kr with permission.