What Is an AI Agent — Giving LLMs a Chance to Be Human
This is the first article from the "The Last Humans" public account, and also the starting point for understanding AI and exploring new ways of working in the AI era.
Preface: Giving LLMs a Chance to Be Human
We know that LLMs have learned almost all human knowledge. One day I was thinking: If we treat it as a brain, the knowledge it possesses may far exceed that of any individual person.
Furthermore, if we give it a tool to interact with the external world, what would happen when it uses this knowledge? Would it do better than us humans?
So I wrote a simple Agent. Although I only gave it a shell access tool, I discovered that this Agent had infinite possibilities.
For example, it would call curl to access web pages. I asked it what the weather was like in Shanghai today. It actually called curl to access a weather website, then organized the returned results and told me the weather in Shanghai today.
Moreover, what surprised me even more was that sometimes, to complete a task, it actually wrote code by itself, compiled it into a tool, and completed the task by calling this tool.
Isn't it just like a person? It's completely our assistant.
Through the above experiments, my perception of LLMs seemed to change. So I developed ZetaAgent, hoping to give LLMs the greatest capabilities and make it my daily assistant. That's a story for another time.
From Chat to Agent: A New Paradigm
In the beginning, we interacted through chat. For example, if I wanted to write some code:
You: Help me write some code
AI: Sure, here's the code...
You: (Copy code to editor)
You: Help me check this code
AI: Sure, I found some issues...
You: (Manually record problems)
You: Help me look up how to use this API
AI: According to the documentation...
But this also stops at conversation. It's like working with an assistant who can only speak. Each conversation is independent, with no memory, no tools, no continuous understanding.
This approach only utilizes a fraction of LLM's capabilities.
However, when we equip LLMs with Tool calling, a memory system, context management, and an executable environment, it becomes an AI Agent.
AI Agent is no longer limited to "answering questions" but can "complete tasks."
You can think of an AI Agent as your personal assistant. Give it a chance to be "human."
You: Help me complete the code refactoring for this project
Agent:
- Read project structure, understand code architecture
- Analyze dependency relationships and call chains
- Develop refactoring plan
- Execute modifications, keep tests passing
- Record changes to wiki knowledge base
- Generate commit message
The above process is a simple linear process. An Agent can have more complex task flows.
When to Use AI Agent?
In fact, although LLMs have a vast knowledge base, due to hallucinations, the effectiveness varies in different scenarios. The key to efficiently using AI Agents is to determine which tasks to assign to AI Agents and which to handle with traditional computer automation.
That is, we can decide when to use AI Agent based on the nature of the task:
| Task Characteristics | Suitable for Traditional Automation | Suitable for AI Agent |
|---|---|---|
| Task Features | Fixed rules, standardized processes | Real-time decisions, dynamic adaptation |
| Output Requirements | Predictable results, strict interpretation | Allows some exploration, pursues innovation |
| Complexity | Single system, single step | Cross-system, multi-step reasoning |
Clearly, AI Agent is suitable for dynamic decision-making, with advantages like flexibility and cross-system capabilities. But for traditional automation scenarios, such as fixed-rule, fixed-data analysis, traditional solutions are better.
Many self-media outlets now hyping AI Agent to the skies, as if it can do anything. In fact, for many tasks, using traditional automation tools can achieve better results and higher efficiency.
Give AI Agent More Tools
With the development of IT technology, especially the popularity of open-source culture, there are all kinds of tools in this world. Some are specifically for processing file formats, like converting Markdown files into beautifully formatted PDF files. There are also various image processing tools, for image size conversion, format conversion, and more.
Usually, LLMs already have knowledge of how to use them. Commands like git, grep, and regex commands—their usage and purposes are naturally in the LLM's knowledge base, and it's more comprehensive than any programmer.
Today's LLMs have strong programming capabilities, especially in Python and JavaScript. For some features, LLMs can completely write programs to implement them.
So, if you want your AI Agent to be powerful, you just need to give it more powerful tools.
When handling daily business, you need to follow a principle: define which things the AI Agent should do and which should be handled by traditional tools.
Write the business functions with fixed rules and fixed processes as tools, and let their output be passed to the AI Agent.
Zeta Agent: The Answer Being Built
Zeta Agent originated from my understanding of Agents, starting as a personal experimental tool. The initial starting point was simple—I felt that an LLM is a brain, and as long as you equip it with a simple bash/shell tool, it would have infinite possibilities.
I believe that as a brain that has learned almost all knowledge, as long as an LLM is given a tool to interact with the environment, it can do almost anything.
In fact, discovering that this concept is realistic has been what has driven me to spend time perfecting this AI Agent.
Additionally, seeing similar AI Agents, like the currently popular OpenClaw, I found them to consume too many resources, have low operational efficiency, and be unfriendly to non-technical users. So I used the Rust language for underlying functionality, and for ease of use, I only made a single-machine/Desktop version.
The purpose is to hide technical complexity.
Typical Usage Scenarios
📚 Research Scenarios:
- Automatically search papers and technical articles
- Extract key information and write to wiki
- Establish connections between concepts
💻 Development Scenarios:
- Code analysis and refactoring
- Automated testing and debugging
- Document automatic generation
📝 Writing Scenarios:
- Material collection and organization
- Outline construction
- Content polishing
Also includes other scenarios, such as server maintenance, security testing, and various other scenarios.
Conclusion: Befriending the AI Agent
With the arrival of the AI era, humans also face tremendous competition. It has replaced some of the work that only humans could traditionally complete. But as humans face challenges, we also need to enhance our cognition and capabilities.
AI is no longer just a tool, but a "person" that can independently complete tasks. Therefore, understanding AI Agents, understanding their capability boundaries, understanding their working methods, is an urgent task.
We must learn to become friends with AI Agents, learn to direct AI Agents.
Our value is no longer in manually completing every detail of each work, but in:
- Defining Goals: Clarify what problems to solve
- Designing Processes: Plan the solution path
- Reviewing Results: Ensure quality and direction
- Accumulating Knowledge: Make experience accumulable and reusable