From Prompt to Agent: The Core Logic of AI Thinking Leap
In the actual AI training in large companies, the fundamental differences between Prompt thinking and Agent thinking are reshaping the way of working. This article deeply analyzes how to upgrade traditional 'literary creation-style' prompts to 'project management-style' Agent architectures, and reveals the practical methodology and pitfall-avoidance guides for building 'digital employee clusters' within large companies.
After doing AI training in large companies for so long, my biggest perception is that Prompt thinking is like 'literary creation', while Agent thinking is like 'project management'.
If you still spend time writing a gorgeous 500-word prompt for AI, trying your luck with 'magic spells', you may be falling into the trap of 'low-level diligence'.
Today, I'll break down this thinking model of transitioning from Prompt to Agent and share some valuable insights that are not easily spread in large companies.
Thinking leap: From 'interviewer' to 'veteran squad leader'
Many people write Prompts with the mindset of an interviewer: throwing out a bunch of requirements, crossing their arms, and waiting for the model to give a perfect answer. If the model doesn't answer well, they keep adding qualifiers and intensifiers, or even resort to threats and inducements.
But in the world of Agents, what you need to do is be the 'formulator of SOP (Standard Operating Procedure)'.
- Prompt thinking: "Please write a in-depth analysis report on the large model industry. It should be professional, in-depth, and 5000 words long." (This is gambling on the upper limit of the model, and the result is often a lot of nonsense.)
- Agent thinking: "I need a report. First, search for industry news in the past 7 days; second, filter out the top 5 financing and technological breakthroughs; third, outline based on these materials; fourth, write and proofread the data one by one."
Valuable insights from large companies: Inside our company, a good Agent design is often 'structured'. Breaking down a complex task into tiny steps that the model can easily handle correctly is much more effective than writing a perfect Prompt.
Remember: Instructions can only solve single-point problems, while workflows can solve the business closed-loop.
Three core elements: In-depth analysis and practice of Agent thinking
To build a real Agent, in essence, is to translate your 'workplace experience' into a set of 'code logic executable by the model'. To make it clear to everyone, let's take the most headache-inducing task for office workers - 'automatically writing weekly reports' - as an example.
1. Logical planning (Planning): From 'one sentence' to 'a map'
In the Prompt era, you thought it was enough to write down the requirements clearly; but in the Agent era, you need to design a 'multi-step reasoning flow'.
Scenario example: If you directly ask the model to write a weekly report, it will probably fabricate content.
Under the Agent architecture, we will introduce the ReAct (Reasoning and Acting) framework. After the Agent receives the instruction, the first step is not to 'write', but to 'think (Thought)'.
It will first generate a task list:
- Extract key performance figures
- Analyze the risk of delay
- Match next week's plan.
In-depth insight: When training the model, we found that long Prompts tend to cause the model to have 'attention deviation'.
The logical planning of an Agent is essentially 'pressure sharing'.
The output generated in the first step serves as the input for the second step.
The error in each link is controlled to the minimum range. This is why the content generated by an Agent is much more reliable than that by a Prompt.
2. Long-term memory (Memory): From 'stranger' to 'personal secretary'
Why does the AI you've trained for a long time become stupid when you switch to a new conversation window? Because it only has a 'fish's memory'.
Practical solution: We will introduce a vector database through RAG (Retrieval Augmented Generation).
- Long-term memory: Store the standard template of the company's weekly reports, your performance goals in the past three months, and even the boss's preference for specific words (for example, the boss doesn't like 'empower' but prefers 'implement').
- Short-term memory: Record your key statements in group chats this week and the temporarily changed requirements.
Effect comparison: When the Agent is about to write a weekly report, it will first 'fish' in the database. It will remember: "In November last year, the boss mentioned in the weekly report approval that he didn't like to see vague words like 'basically completed' and required specific percentages." This kind of 'rule-abiding' output is the core competitiveness in the workplace.
3. Tool use (Tool Use): Let AI have 'administrator privileges'
This is the dividing line between an Agent and a 'chatbot': it can not only write, but also act.
Scenario example: When writing a weekly report, if the Agent encounters 'performance data', it no longer 'guesses', but conducts external function calling (Function Calling):
Step 1: Data access (Data Sourcing)
The Agent uses the Feishu API (or a specific data plugin) as a 'digital antenna' to directly access the specified Feishu spreadsheet.
Action: Real-time capture the row and column information in the spreadsheet (such as project names, progress percentages, and deadlines).
Result: Convert the messy online spreadsheet into structured data (JSON format) that the Agent can understand.
Step 2: Intelligent intention recognition (LLM Reasoning)
The Agent hands the captured data to the large language model (LLM) for 'reading', and the LLM acts as a data analyst.
Action: The LLM determines the core value of the current data.
- If it is to compare progress, it will decide to draw a 'bar chart';
- If it is to show the time trend, it will decide to draw a 'line chart'.
Result: Generate an instruction list containing 'chart type, X-axis field, Y-axis field, chart title'.
Step 3: Drawing tool call (Tool Execution)
The Agent calls the pre-installed visualization tool library on the server (such as Matplotlib or Seaborn in Python) according to the instruction list.
Action: This is like giving an automatic instruction to the drawing software. The tool library runs silently in the background and performs pixel-level rendering according to the parameters in the second step.
Result: Generate a high-definition .png or .jpg chart image.
Step 4: Report synthesis and output (Delivery)
The Agent arranges and integrates the generated chart image with the text summary automatically written by AI.
Action: Insert the chart into the weekly report template, or directly send the chart to the Feishu group chat through a bot (Webhook).
Result: What the user finally sees is an automated weekly report with both pictures and in-depth analysis.
A good Agent should have the 'right to choose tools'.
We should let the model raise its hand and say actively when it's not sure: "I need to call the 'database query tool' to ensure the accuracy of the numbers. Please authorize." This transformation from 'ignorance' to 'knowing when to stop' is a sign of AI advancement.
Pitfall-avoidance guide: Lessons from 'failures' in large companies
Although Agents are powerful, we've encountered countless pitfalls within large companies. Here are three suggestions for those who want to make the transformation:
- Beware of 'over-engineering': Not all scenarios are suitable for Agents. If it's a simple translation task and you insist on designing four steps of 'planning - translation - reflection - proofreading', it will not only be slow, but also increase the Token cost by ten times. If a single Prompt can solve the problem, don't use an Agent.
- Recursion of hallucinations: The more steps an Agent has, the easier it is for errors to accumulate. If the first step of planning is wrong, all subsequent steps will be wrong. Therefore, when designing an Agent, 'manual confirmation points' or 'logical gates' must be set to make it stop and ask you at key nodes.
- Don't blindly believe in the model's 'self-evaluation': Many students like to add a sentence 'Please check your mistakes'. In fact, if the model is wrong from the beginning, it will probably think it's 'reasonably wrong' when checking. Effective self-reflection requires objective verification with the help of external tools (such as Linters and code interpreters).
Conclusion: The 'dividing line' in the workplace in the AI era
As an AI trainer, I think every day: When the model's ability becomes stronger and stronger, where lies the value of humans?
I've found that in the future AI workplace, people will quickly be divided into two groups: One group is still practicing 'magic spells' hard, trying their luck with mysterious Prompts, and their ceiling is the original upper limit of the model. The other group has started to build their own **'digital employee clusters'. They are no longer just simple executors, but 'AI architects'.
Prompt determines the ceiling of AI, but Agent thinking determines your business foundation.
When you start thinking about 'how to manage the execution process of AI' instead of 'how to beg for the result from AI', you've already got the admission ticket to the high-level AI players.
This article is from the WeChat official account "Everyone is a Product Manager" (ID: woshipm), author: Mr.Right., published by 36Kr with authorization.