Why does the AI agent appear suddenly, and why is it irreversible?

Writing by: Zhang Feng

1. AI Becomes “Agent Users,” Defining New Boundaries for Human-Machine Collaboration

Recently, Microsoft previewed a new type of AI agent called “Agentic Users” in its product roadmap. These agents will have dedicated email accounts and can autonomously participate in meetings and handle tasks. This marks AI’s evolution from a passive tool to an active collaborator with a certain “agent” identity. This shift is not an isolated event but a natural result of long-term investments by tech giants like Microsoft in the AI Agent (intelligent agent) field. Microsoft defines AI Agents as intelligent systems capable of automating repetitive, low-error tasks through coding and execution, thereby unlocking value in scenarios like finance and education that require large data processing and precise calculations.

2. However, as AI Agents become increasingly autonomous and even begin to simulate human employee “identities,” a series of fundamental questions arise: How will highly autonomous AI influence existing workflows and decision-making mechanisms in cutting-edge fields like quantum networks and digital finance? Does the concept of “Rotifer Autonomous Evolution Protocol” suggest that AI will self-evolve beyond preset paths? In today’s landscape where digital governance and compliance frameworks are still imperfect, how should we build rules to ensure the prosperity of open-source tech ecosystems while avoiding out-of-control risks? These questions point to a core issue: we are at a critical juncture in the paradigm shift of human-machine relationships, urgently needing a clear blueprint for the upcoming “intelligent agent society.”

3. The Evolution from Automation Scripts to “Agent Users”

The concept of AI Agents did not emerge overnight; its development closely follows the past decade of artificial intelligence progress, especially the leap in large language models (LLMs). Microsoft research indicates that, by extracting logical reasoning from data, large language models support complex decision-making and autonomous task execution, enabling them to act as intelligent agents in various workflows. This technological foundation has allowed AI to evolve from executing simple, fixed automation scripts (like traditional RPA robots) to understanding natural language instructions, planning, and executing multi-step tasks as “intelligent agents.”

4. Looking back at Microsoft’s practical path, this evolution is clear. Early applications focused on improving efficiency in specific scenarios, such as in healthcare, where intelligent Power Automate RPA workflows integrated hospital information systems (HIS) to replace large-scale repetitive administrative work, thereby improving resource utilization. This can be seen as the prototype of AI Agents—automation focused on specific tasks. As technology matured, the focus shifted toward building more general and autonomous agent frameworks. Microsoft provides open-source tools and SDKs like AutoGen and Semantic Kernel at the Infrastructure-as-a-Service (IaaS) level, aiming to offer enterprise-ready, stable intelligent agent development solutions.

5. The peak of development lies in exploring “embodied intelligence” and general-purpose agents. Microsoft research published forward-looking papers on “Agent AI,” attempting to pre-train foundational models for developing general AI agents by integrating embodied data from fields like robotics. From efficiency tools to programmable frameworks, and now to pursuit of generality and autonomy as “agent users,” AI Agents have completed a ten-year journey from “tech” to “way,” laying both historical and technological foundations for today’s widespread applications.

6. The Wave of Agent Development Driven by Technological Breakthroughs, Business Needs, and Ecosystem Competition

Why has AI Agent suddenly become a focus of industry at this particular moment? Behind this are the intertwined and resonant forces of technology, demand, and ecosystem.

7. First, continuous breakthroughs in core technology are the fundamental driver. Advances in large language models in code generation (e.g., WaveCoder), logical reasoning, and contextual understanding have endowed AI Agents with “brains.” Cloud computing platforms provide powerful computing power and stable environments, while open-source frameworks significantly lower development barriers. For example, Microsoft’s Semantic Kernel enables developers to more easily build intelligent agents that understand semantics, invoke external tools, and APIs. These technological advances collectively address key questions of “can AI think” and “how to act.”

8. Second, urgent market demand for cost reduction, efficiency, and digital transformation fuels the push. In an increasingly competitive global market, enterprises seek to free employees from repetitive, low-value tasks to focus on innovation and strategic decisions. AI Agents excel here, capable of maintaining high efficiency and low error rates while processing vast data and performing precise calculations. From risk modeling in finance to process optimization in manufacturing, intelligent agents are becoming core engines for unlocking data potential and building smart applications. Industry events like Microsoft AI Summit Taipei highlight the strong industry expectation for new human-machine collaboration paradigms centered on AI Agents.

9. Lastly, strategic positioning in future ecosystems creates competitive momentum. AI Agents are viewed as the next-generation interface and operating system for human-computer interaction. Whoever controls the dominant platform and protocols for intelligent agents may occupy a hub position in future digital ecosystems. Microsoft actively promotes its Copilot and Agent ecosystem, hosting ongoing developer events like “Microsoft AI Genius” series to consolidate its full-stack advantages—from development tools to cloud platforms—and to gather developer communities, fostering a thriving ecosystem of intelligent agent applications. This platform-level competition accelerates the transition of AI Agent technology from labs to industry.

10. Building a “Framework-Evolution-Governance” Three-in-One Intelligent Agent Development System

Faced with opportunities and challenges brought by AI Agents, we need a systematic solution rather than piecemeal technical fixes. This system should encompass technological frameworks, evolution mechanisms, and governance rules across three levels.

11. First, rely on robust open-source frameworks to lower application barriers and ensure safety and control. Enterprises should avoid reinventing the wheel when adopting AI Agents and instead base their development on validated open-source frameworks. As provided by Microsoft’s AutoGen and Semantic Kernel, these officially supported tools offer ready-to-use, stable solutions. They define standard ways for intelligent agents to interact with the external world (e.g., via MCP-model context protocols), but current protocols also have security limitations that need community-driven improvements. Enterprises can leverage these foundations, combined with their expertise in digital finance, quantum network simulation, and other fields, to develop vertical-specific intelligent agents for rapid, secure deployment.

12. Second, explore controlled autonomous evolution protocols to guide AI capabilities growth. Concepts like the “Rotifer Autonomous Evolution Protocol” represent frontier directions where AI self-learns and iteratively optimizes within specific environments. The key is “control”: setting clear evolution goals and boundary rules in highly simulated digital twin environments (e.g., virtual financial markets, quantum computing networks), allowing agents to explore strategies via reinforcement learning. This accelerates AI’s application capabilities in complex domains while keeping evolution within safe sandbox environments, providing valuable data on behavior patterns.

13. Third, establish forward-looking digital governance and compliance frameworks to regulate the societal integration of intelligent agents. When AI Agents become “agent users,” existing legal and ethical frameworks face direct challenges. Solutions must be preemptive, including defining legal responsibilities (developer, user, or the agent itself?), establishing audit and traceability mechanisms for decision transparency in critical areas like finance, and setting data privacy and security standards to prevent misuse. Building governance frameworks requires collaboration among technologists, legal scholars, policymakers, and industry leaders, ideally integrated into open-source ecosystems, embodying “governance as code.”

14. AI Agents Are Irreversible: Ensuring Safety, Inclusiveness, and Goodness

The romanticism around AI Agents is now irreversible. While actively deploying them, we must remain clear-headed to avoid potential traps and risks.

15. First, beware of the illusion of “full autonomy” and adhere to the fundamental principle of human-in-the-loop. No matter how intelligent AI Agents become, they are ultimately extensions of human intent and design. Microsoft’s “agent user” concept aims to improve “human-machine collaboration” efficiency. We must avoid designing or deploying fully autonomous, goal-setting “strong autonomous intelligent agents” that operate without human oversight. Critical decisions—such as in medical diagnosis, financial risk control, or judicial assessments—must retain human experts’ final review and veto power. Architecturally, systems should include “kill switches” and intervention channels.

16. Second, prevent risks of widening technological gaps and ecosystem lock-in. Powerful AI Agent platforms and frameworks may be dominated by a few tech giants, potentially marginalizing small and medium enterprises due to high technical and financial barriers, thus exacerbating digital divides. Over-reliance on closed ecosystems from single vendors also risks lock-in. While embracing excellent solutions from companies like Microsoft, the industry should actively promote cross-platform interoperability standards and encourage diverse, open-source ecosystems to ensure healthy competition and innovation.

17. Third, pay attention to employment transformation and social adaptation challenges. Automation by AI Agents will inevitably impact existing jobs. Society must not only focus on deploying technology but also plan for workforce retraining and educational reforms. Future education should emphasize creativity, critical thinking, and AI collaboration skills to help workers adapt to new human-AI co-working models. Enterprises should also take responsibility for providing pathways for affected employees’ transition.

18. Fourth, ethical and bias issues will be amplified by autonomy, requiring ongoing governance. AI Agents trained on data and interactive learning may inherit or magnify societal biases and injustices. As they gain more decision-making autonomy, these risks increase. Therefore, ethical review and bias detection must be continuous throughout development, deployment, and evolution, becoming an ongoing governance task rather than a one-time certification.

19. Looking ahead, the evolution of AI Agents is irreversible, opening a new chapter in intelligent applications. The success of this transformation depends not only on elegant code and powerful algorithms but also on our ability to build a safe, inclusive, and benevolent development framework with a high sense of responsibility and foresight. Only then can intelligent agents truly become effective partners in expanding human cognition and solving complex challenges, jointly advancing toward a more efficient and creative future.