Discussing Agentic AI Through Historical Cycles

Throughout human history, every major technological breakthrough has reshaped production relationships and unleashed new productivity. Today, AI is sparking a silent revolution: it aims not to overthrow but to become a new bridge connecting humanity with the existing technological system, releasing the value accumulated over decades in the software ecosystem. This article will take you deep into understanding the essence of this revolution and how it will change our work and lives.

Historical Insights: Value Release Patterns in Technological Revolutions

The development of technological revolutions often follows similar patterns, with each significant breakthrough redefining the relationship between humans and tools. Let us understand this through the long river of history:

In the 1920s, the electric revolution transformed factory production. Initially, factory owners simply replaced steam engines with electric motors, but the efficiency improvement was limited. It was only when engineers realized that the true value of electricity lay in redesigning factory layouts, allowing each machine to have its own motor, that a qualitative leap in production efficiency was achieved.

In the 1980s, personal computers began to enter offices, but the initial usage was merely treating them as expensive typewriters. The real productivity revolution awaited the emergence of the graphical user interface (GUI), which enabled ordinary people without programming knowledge to harness the power of computers.

These historical lessons tell us that the key to a technological revolution often lies not in creating entirely new production tools but in finding better ways to unleash the potential of existing technologies.

Current Dilemma: Software Ecosystem Locked by Expertise

Today, humanity has built a software ecosystem valued at trillions of dollars. Let us take a look at the specific value release situation:

The current specialized software faces serious usage barrier issues:

Adobe Creative Suite:

• Photoshop has over 1000 functional options
• A professional designer needs 3-5 years to master this set of tools
• Millions of people give up each year due to the learning barrier

Enterprise Resource Planning (ERP):

• SAP’s complete training course requires over 200 hours
• Implementing an ERP project in a large enterprise usually takes 2-3 years
• Statistics show that about 40% of enterprises fail during implementation due to complexity

These data tell us that the value of software is being constrained by the high barriers of expertise. It is like a gold mine, where treasures are there, but ordinary people cannot extract them.

AI: The “Knowledge Compiler” of the New Era

AI is changing all of this in a revolutionary way. It acts like an intelligent translator, capable of converting human natural language demands into instructions that specialized software can understand and execute:

Let us look at some ongoing innovations:

Microsoft’s Copilot series:

• Word Copilot can directly understand instructions like “give me a project summary”
• Excel Copilot can translate “analyze sales trends” into complex data processing workflows
• Users report a 35-50% increase in work efficiency

Cursor’s code collaboration:

• Transforms demands like “create a to-do list app” into complete code
• Automatically handles dependency management and error fixing
• Development efficiency is reported to increase by 3-5 times

Agentic AI: The Technical Core of Paradigm Shift

Agentic AI represents a completely new way of working, acting like a “steward” of the digital world, capable of understanding, planning, and executing complex tasks:

1. Tool Invocation Ability: The All-Powerful Steward of the Digital World

Case Study: Automation of Video Production Process

• User demand: “Produce a promotional video for a product”
• AI execution process:
• Invoke Photoshop to process product images
• Use Premiere to edit video clips
• Add special effects through After Effects
• Process audio with Audition
• The final user only needs to review and fine-tune

2. Multi-Step Decision-Making Ability: The Orchestrator of Complex Tasks

Case Study: Automatic Generation of Enterprise Quarterly Reports

• Step 1: Collect data from multiple systems
• Step 2: Analyze data using Excel
• Step 3: Generate charts and visualizations
• Step 4: Organize content in Word
• Step 5: Create PPT presentations

The entire process resembles an experienced project manager scheduling resources.

3. Self-Iteration Ability: An Evolving Intelligent Assistant

Case Study: Self-Improvement in Code Development Process

• Learn and improve code from error logs
• Optimize algorithms based on performance data
• Adapt to new programming paradigms
• Integrate user feedback to improve solutions

This wave of AI transformation has birthed a new paradigm of “scheduling computing power with natural language,” a breakthrough comparable to the invention of the graphical user interface (GUI), and it may even become the most transformative technological leap in human history.

In the coming period, to fully unleash the potential of AI, the key lies in deeply integrating it with the existing trillion-dollar software ecosystem, liberating the value that has been sealed for decades.

This integration will be achieved through the agent model—allowing large language models (LLMs) to respond precisely to user needs with programming capabilities, combined with APIs and GUIs.

From this perspective, the limitations of ChatGPT do not stem from its Q&A mode but from its failure to organically integrate the kernel into the product. Users can only obtain code snippets through conversation and then must switch to a local environment or other platforms (like Deepnote) to execute, with humans acting as the connecting link.

This fragmented user experience severely restricts efficiency.

While Operator has opened up new possibilities, a more ideal solution would be to directly integrate the kernel into the model, upgrading the ChatGPT interface from a simple dialogue box to an intelligent orchestration platform for LLMs and tools. I believe that by 2025, OpenAI and Anthropic will recognize and achieve this breakthrough, ushering in the era of agentic AI.

At the same time, this Spring Festival, with the explosive popularity of Deepseek, I have also seen an endless stream of large model SOTA rankings, and the evaluation of large models is a complex, multi-dimensional, and dynamic process. If we make absolute judgments about the quality of models based solely on benchmarks or individual cases without considering specific application scenarios, such evaluations lack reference value.

What we should really focus on is how different models match specific tasks and scenarios, and how to more effectively leverage the unique advantages of each model.

Potential Risks and Response Strategies

While embracing this wave of transformation, we must also be vigilant about several key risks:

1. Risk of Over-Reliance

• Manifestation: Loss of understanding of underlying tools
• Response: Maintain learning and training of core competencies

1. Security Risks

• Manifestation: AI may make mistakes when invoking tools
• Response: Establish multi-layered validation mechanisms

1. Knowledge Accumulation Risks

• Manifestation: Experience and best practices may not be effectively accumulated
• Response: Establish a knowledge management system and conduct regular reviews

Imagining Future Work Scenarios

Let us imagine a future work style through a specific scenario:

Xiao Ming is a marketing manager who needs to prepare a launch plan for a new product. He tells AI: “We are launching a new smart watch next month, help me prepare a complete launch plan.”

The AI will:

1. Analyze market data and generate a competitive analysis report
2. Design marketing materials, including posters and promotional videos
3. Plan social media advertising strategies
4. Predict sales trends and develop pricing strategies
5. Generate a complete project timeline

The entire process may only take half a day, rather than the traditional two weeks.

Reflection and Action

Let us conclude this article with several key questions that everyone should ponder deeply:

1. In your field, which work processes most need AI empowerment?
2. What is your core competitiveness? How will the prevalence of AI affect it?
3. How to maintain an understanding of underlying principles while improving efficiency?
4. How do you plan to adjust your learning and working methods to adapt to this new era?

Conclusion

The uniqueness of this revolution lies in the fact that it does not aim to replace existing tools but rather to make these tools available to more people. It is not creating a new gold mine; instead, it is providing everyone with the ability to mine the existing gold mine.

At this turning point, the most important thing is not to be deceived by the superficiality of technology but to understand the essence of this transformation: it is redefining the relationship between humans and tools and making technology truly enter everyone’s life. For individuals, this is an opportunity that requires active preparation; for organizations, this is a challenge that must be taken seriously; for society as a whole, this could be a historic opportunity for the true democratization of productivity tools.