The development of global AI Agents (artificial intelligence) is accelerating, and its technological breakthroughs, application scenario expansion and industrial ecosystem construction are jointly driving this field into a new stage.
The following analyzes its development trends from four dimensions: technology, application, industry and challenges:
I. Technology development trends
1. Enhanced autonomy and collaboration
AI Agents are evolving from single-task execution to multi-agent collaboration. For example, Google's A2A protocol and Anthropic's MCP standard achieve seamless collaboration between different AI systems through unified communication protocols. This capability will drive AI from a "tool" to a "team member" and achieve more efficient collaborative work in complex scenarios.
2. Breakthrough in cross-modal understanding and generation capabilities
The advancement of multimodal large models (such as GPT-4o) enables AI Agents to simultaneously process multiple data types such as text, images, and voice. For example, OpenAI's ComputerUse technology can directly operate computer interfaces, and MobileFlow achieves cross-platform compatibility through hybrid visual encoders. This capability will greatly expand the interactive boundaries of AI in the physical world.
3. Interaction between embodied intelligence and the physical world
Embodied AI has become a research hotspot. For example, Yunshen Technology's four-legged robot "Jueying" has achieved fully automatic inspection of substations. The combination of AI Agents and robotics technology will promote its implementation in manufacturing, logistics, agriculture and other fields.
4. Maturity of enterprise-level AI tools
Enterprise-level AI Agents (such as Jinzhiwei's Ki-AgentS) achieve full process automation from task planning to result verification by combining RPA (robotic process automation) with large model capabilities. Such tools have demonstrated high reliability in the fields of finance, government affairs, etc., promoting the evolution of AI from "cost reduction and efficiency improvement" to "reshaping business".
II. Application scenario expansion trend
1. The outbreak of consumer-level AI
2026 is expected to be the outbreak year of consumer-level AI Agents. Such products will undertake tasks such as automatic code writing, scheduling, and booking travel, truly realizing the value of "completing tasks for users". For example, Cursor, a company valued at $2.5 billion, has helped engineers write code, with annualized revenue exceeding $100 million.
2. Deep integration of medical and scientific research
In the medical field, AI Agents are transforming from auxiliary diagnosis to "intelligent collaborators". For example, the "Meta-Intelligence" medical model released by United Imaging covers multiple scenarios such as imaging diagnosis and clinical treatment. In the field of scientific research, AI Agents accelerate scientific discovery through automated experimental design and data analysis.
3. Comprehensive penetration of smart terminals
Breakthroughs in end-side AI have enabled terminal devices such as smart cars and smart phones to have stronger localized AI capabilities. For example, Black Sesame Smart's Huashan A2000 chip supports Transformer architecture optimization, allowing end-side models to achieve multi-modal processing at low power consumption.
III. Trends in industrial ecosystem construction
1. Prosperity of open source ecosystem
Open source large models represented by DeepSeek have lowered the technical threshold and promoted the rapid popularization of AI Agents. For example, the 264-page review jointly released by MetaGPT, Microsoft and other institutions provides developers with a modular design framework.
2. Competition for standard protocols
Technology giants compete for dominance in the AI ecosystem through protocol standards. For example, Google's A2A protocol focuses on intelligent collaboration, while Anthropic's MCP standard unifies the connection between AI and external tools. This competition will reshape the value distribution of the global AI industry chain.
3. Differentiated competition among Chinese companies
Chinese AI Agents companies have achieved overtaking by deepening their scene cultivation. For example, Jinzhiwei's accumulated experience in "financial-grade security" in the financial field has enabled its products to have enterprise-level reliability; Manus quickly detonated social networks through its "zero-threshold AI assistant" positioning.
IV. Challenges and responses
1. Accuracy and security
AI Agents' "illusion" problems (such as generating false information) and security vulnerabilities remain core challenges. For example, large models in the medical field need to improve their professionalism through vertical data training, and enterprise-level AI tools need to embed the controllable features of RPA to ensure execution safety.
2. Ethics and regulation
With the application of AI Agents in key areas, issues such as data privacy and algorithm bias have attracted attention. For example, the EU's Artificial Intelligence Act imposes strict regulatory requirements on high-risk AI systems and promotes the industry to establish a responsible AI development framework.
3. Technology inclusion and fairness
Uneven distribution of computing resources may exacerbate the digital divide. For example, Parallel Technology reduces computing costs through a computing network service platform, helps small and medium-sized enterprises use AI technology, and promotes technology inclusion.
Future Outlook
The development of global AI Agents will show a spiral upward trend of "technological breakthrough-scenario implementation-ecological reconstruction". 2025 is regarded as a turning point for AI Agents, and its core value will shift from "replacing manpower" to "reconstructing productivity".
The innovation of Chinese companies in computing infrastructure, end-side AI, smart terminals and other fields is expected to reshape the global AI industry landscape. However, this process needs to be based on solving challenges such as technical reliability and ethical compliance, and ultimately achieve the symbiosis and prosperity of AI and human society.