How can professionals make good use of AI Agent? The key shift from asking AI to letting AI do it for you

Do you have this experience?

Opened ChatGPT, asked a question, and got a pretty good answer. Then what?

You copy and paste it into the file, adjust the format, go to another system to check the information, then integrate the content from both sides, and finally manually send an email to notify the relevant personnel…

In the whole process, AI actually only helps you with the first step. The remaining 80% of the work is still done by yourself.

This has been my real-life experience with ChatGPT over the past few years. It is indeed very powerful and can answer questions, write copy, and translate documents, but it always feels like it is one step behind - it always stays in a state of waiting for you to ask questions instead of actually helping you complete the work.

It wasn’t until I started to come into contact with AI Agent that I discovered that this gap is actually a fundamental difference.

Chatbot answers questions, Agent completes work

A traditional Chatbot (like the way ChatGPT is generally used) is like a knowledgeable librarian. You ask him any question and he can give you the answer and even help you organize it into a beautiful format. But here’s the thing: you have to walk to the library yourself, ask the questions yourself, and take the answers back to apply yourself.

AI Agent is different. It’s more like a full-time assistant that not only answers questions, but also takes the initiative to run errands for you. You tell it the goal, and it will break down the task, mobilize resources, execute the steps, and finally give you the result.

According to Google Cloud 2026 AI Agent Trends Report, the core difference between the two can be understood as follows:

Chatbot

• Operation mode: passive response, waiting for you to ask questions
• Workflow: linear - input → output → end
• Essential positioning: Answer questions

AI Agent

• Operation mode: Take the initiative to execute and do it yourself after understanding the goal
• Work flow: cyclical - observe → think → act → evaluate → recycle
• Essential positioning: get the job done

To be more specific, the Chatbot process is that you ask once, it answers once, and then it’s over. But the Agent will enter a self-circulating, self-correcting system - sensing the environment, thinking about strategies, taking action, and evaluating the results. If the results are not ideal, it will automatically adjust and try again.

This difference will bring about a completely different experience in actual work scenarios.

Three scenarios where professionals should give priority to importing Agent

Since Agents can do work rather than just answer questions, what tasks are best suited for Agents to do?

The following three scenarios are particularly worth trying first:

Scenario 1: Work that requires cyclic monitoring

Typical examples include: advertising effectiveness optimization, community public opinion monitoring, and competitor tracking.

The characteristics of this type of work are: the need to continuously observe data, analyze changes, take action, and then observe the effects. The traditional approach is manual regular inspection, but Agent can perform a cycle of monitoring → analysis → action → monitoring again 24 hours a day.

My own example is using AI Agent to monitor OKR progress.

Here’s the thing: Once AI directly pointed out in the conversation that the scheduling and automation I am currently doing are still at the tactical level, lacking strategic level planning. It suggested that I set up a quarterly OKR structure in Anytype:

Quarterly OKR

• O1: Content brand influence
• KR1: vista.tw monthly traffic target
• KR2: Number of newsletter subscriptions
• KR3: Number of articles published per month
• O2: Product Revenue
• KR1: Online learning platform launched
• KR2: Copywriting health check tool paid service
• KR3: Workshop sessions
• O3: System efficiency
• KR1: Automation coverage
• KR2: average time from idea to publication for each article

In practice, I wrote the current quarter OKR summary into CLAUDE.md so that every time I start an AI session, it can automatically align the goals. The morning and evening report schedules I set will also remind me of the current progress based on OKRs.

This is the difference between Agent and Chatbot: Chatbot waits for you to ask questions before answering; Agent will proactively remind you what you have not done or where you have deviated from the direction based on your goals.

A marketing manager told me that he now directs a team of agents: the data agent is responsible for screening market trends, the analysis agent is responsible for monitoring advertising data, and the content agent is responsible for drafting social posts. Each Agent performs its own duties and only needs to make final decisions at key nodes.

Scenario 2: Cross-system integration process

The pain point in many workplace jobs is not how difficult a single task is, but the need to move data between different systems, confirm status, and trigger actions.

For example: the newcomer registration process. The traditional approach is for HR to manually notify IT to open permissions, send welcome emails, arrange department introductions, and update the personnel system. Each step is easy, but it adds up to a chore.

After the Agent is imported, this process can become: the Agent detects the new employee’s registration date, automatically notifies IT to open an account, automatically sends a welcome letter, automatically arranges the onboarding schedule, and automatically updates related systems. HR just needs to confirm at the end that everything went well.

Scenario 3: Tasks that require error retry mechanism

Some tasks will encounter exceptions: system crashes, API return errors, and incorrect file formats. Chatbot usually stops when encountering this situation, requiring manual intervention.

But the Agent has the ability to handle these exceptions. It can detect errors, try alternatives, automatically retry, and even notify you to intervene after multiple failures.

This fault tolerance is particularly important when processing large amounts of data or long-running tasks. For example, batch processing reports, regular database synchronization, or automated customer service response systems.

From Executor to Supervisor: Your role is changing

But you may be thinking: If Agent is so powerful, then what is my value?

This is the most critical shift in thinking in the workplace in 2026.

According to Google’s predictions, this year, the role of employees will massively shift from people performing tedious tasks themselves to human overseers of AI agents.你的主要职责不再是自己做每一件事，而是：

• Set strategy: Tell Agent what the goal is and what the priority is.
• Define boundaries: Set the scope of Agent’s authority, which things can be decided by oneself, and which ones need to be reported back
• Quality Verification: Final review and control of Agent’s output
• Exception Handling: Handle complex situations that the Agent cannot handle

换句话说，你的价值从会做事升级为会指挥 AI 做事。 This does not make you less important, but it makes your professional judgment and strategic thinking more important.

入门建议：从哪里开始？

Don’t know where to start? Here’s my suggestion:

Step 1: Take inventory of your repetitive tasks

Take half an hour, take out a piece of paper or open a note-taking software, and make a list of your weekly tasks. Without overthinking it, start by writing down any tasks that feel mechanical.

Next, use the following three questions to filter:

1. **Does this require switching between multiple systems? ** For example: Copy data from email to spreadsheet, then organize it from spreadsheet and paste it into report. Agent is particularly good at this kind of porter job.

2. **Does this require regular inspection and response? ** For example: read social messages every morning, check inventory quantity once a week, and summarize the previous month’s data at the beginning of each month. For this kind of monitoring work, Agent can help you keep an eye on it 24 hours a day.

3. **The steps for this matter are fixed, but are they trivial to execute? ** For example: after receiving a customer inquiry letter, you need to check the inventory, calculate the quotation, send a reply letter, and update the CRM. Each step is not difficult, but it is very annoying to string together. Agent can automatically connect these steps.

The more conditions a task meets, the more suitable it is to import Agent first.

Step 2: Choose a low-risk test scenario

Once you find a suitable task, don’t rush to fully import it yet. Choose a scenario where you won’t cause major losses if you make a mistake to practice.

Good test scenarios usually have these characteristics:

• Small scope of influence: Only involves yourself or a small team, and will not directly affect customers or company operations.
• Large fault tolerance: Even if the Agent makes an error, you have time to discover and correct it without causing irreversible consequences.
• Short feedback cycle: You can see the results quickly, making it easy for you to adjust and optimize

举几个适合新手的试验场景：

• Automatically organize daily meeting minutes and send them to the designated mailbox
• Regularly extract industry news from specific websites and compile it into summaries
• Automatically identify received business card photos and organize them into address book format
• Automatically generate working hours statistics for the week every Friday afternoon

Even if you make mistakes in these tasks, you can at most just redo them once, and there won’t be any big problems. But through these exercises, you will begin to understand the operating logic and limitations of Agent.

Step 3: Learn to command rather than operate

This step is the most critical and most easily overlooked.

To use an Agent’s mindset, you must shift from an operational tool to a command assistant. A tool is what you tell it to do, an assistant is what you tell it to achieve.

Specifically, you need to practice these four things:

1. Clearly express the goal: Don’t say “Help me process this data”, say “Category this customer list in Excel by region, and mark the customers who have not repurchased within three months.” The clearer the goal, the more in line with expectations the Agent’s output will be.

2. Set reasonable expectations: Agent is not omnipotent. It’s good at handling tasks that are structured and have clear rules, but when it comes to work that requires creative judgment or interpersonal interaction, you still need to check it.

3. Establish a checking mechanism: Don’t let the Agent be completely automated and then ignore it. Set key nodes to report progress, or conduct regular spot checks on output quality. Trust is built slowly.

4. Give specific feedback: If the Agent’s output does not meet expectations, don’t just say “it’s not right”, explain “what’s wrong” and “what should be done right”. Good feedback can make the Agent (or the process you design) more and more accurate.

This is not a replacement, but an upgrade

Back to the scene at the beginning of the article: you asked questions using ChatGPT, and then completed the remaining 80% of the work yourself.

Now, you have another option: let AI Agent do the 80% for you.

This doesn’t mean you become unimportant. In fact, when the Agent handles those repetitive, mechanical, and cross-system chores, you have more time to focus on things that really require human judgment: strategic planning, creative thinking, relationship management, and those human touches that AI cannot learn.

In 2026, AI agents are no longer a concept in science fiction, but a reality that is changing the way we work. The question is not whether to use it, but how to use it well.

Perhaps now is a good time to start thinking about this question.

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If you want to see more sharing of practical AI applications, you are also welcome to read these articles I have written before:

• When AI not only helps you write, but also helps you research, format and publish: the content production line I created with Claude Code
• Anytype + Claude: Create an AI-driven second brain to make your notes come alive

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