This is the 461st original article by Chaozi.
The combination of AI agents and large models opens up vast possibilities. Recent research on AI has led us to a key value point: building an agent tailored to you and your industry.
I shared these research thoughts with some friends and realized I still hadn’t explained it clearly. Today, I’ll try to explain it in plain language to see if everyone can understand. If it becomes clearer, please let me know in the comments.
For example, if I input a command into a general large language model (LLM), asking it to write an article on why we need agents, it can produce a draft in under 30 seconds, making me feel like I’ve gained valuable insights. But if I let my AI agent handle the same task…
Its workflow involves planning first, then researching and gathering information, making decisions, reflecting and adjusting, and finally collaborating with multiple agents before executing and delivering the output.
Can you see the difference?
For simple problems, we can just provide a prompt and let the large model handle it easily.
However, when faced with a more complex problem, such as creating an IP for a boss, the task becomes more complicated. You need to research the industry, define the IP positioning, design the business model, plan the product, design the content, implement the content, manage accounts, analyze data, build a dual-domain operational system, and standardize the model, all requiring a systematic approach.
Each part becomes complex, and each step needs to be designed carefully. At this point, the prompt needs to be broken down into different steps.
Imagine there are ten steps, logically ordered from 1 to 10. If the tenth step goes wrong, based on the ninth step, the ninth step must also be adjusted, and this adjustment must be pushed back up, requiring multiple iterations. Thus, if any step has an issue, the large model’s answer won’t meet our needs.
In this case, we can treat every two steps as a separate agent, allowing each agent to handle its specialized part. By connecting these agents to address a complex problem and then passing the results to the large model for analysis, the answers we get will be much closer to what we want.
The perception, decision-making, and execution capabilities of agents, along with continuous memory and iterative corrections, enable the large model to interact more directly with users and the environment, thereby creating greater value.
If you build an AI agent for your industry and continuously iterate and refine it, the value it brings you will not only be cost reduction and efficiency improvement but also a revolutionary leap in thinking.
Wishing you a fruitful day 🎁
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