Some thoughts on AI Agent product management

【Thinking 1】  There is always a huge cognitive gap and a gap in expression between the complex needs of users and the capabilities of a single underlying AI model. It is not easy to make the model understand human intentions and for users to understand the working mechanism of the model.

 【Thinking 2】  Agent, as an intermediary between users and models, its value is to fill and manage this dynamically changing gap.

【Thinking 3】 The foundation of a good agent comes from a deep insight into user intent , management ideas, and an understanding of the capabilities of AI models. The “good” here means having relatively good user value. Whether it can make money and has commercial value is another more complicated topic.

【Thinking 4】 Traditional software design is oriented towards “determinism” (fixed input, output, tasks and measurement standards). Agents need to have “reasoning” capabilities to cope with complex scenarios.

Therefore, the core work of the Agent product manager has shifted from planning "functions and data flows" to planning "roles, responsibility boundaries and core capabilities" (achieved through prompt words) .

This is also the explanation of management thinking in the third article. The foundation of management is to give full play to the role of people. To give full play to the value of AI LLM, LLM should be regarded as a "person" rather than a functional module.

【Thinking 5】 The gap between technological evolution and user expectations is constantly changing, which constitutes the core value space of Agent product managers.

【Thinking 6】 The job of an agent product manager is to understand user needs with the left hand (deep understanding of goals and intentions), and to control the implementation path with the right hand (master the way agents are built).

【Thinking 7】 The key difference between AI interaction (Agent) and traditional software interaction lies in the design of the system prompt. I think that positioning LLM as "omnipotent and omniscient" is an illusion, a mirage, and narrow-minded. Even if LLM capabilities continue to develop by leaps and bounds, the Agent paradigm is still needed to release the value of LLM.

[Thinking 8] AI Agent product design involves a wide range of aspects, such as data, models, processes, and tool chains. Good system prompt word design will reflect the reserves of these capabilities.

【Thinking 9】 The “ease of use” of AI/Agent is directly reflected in the quality of natural language interaction of users. System prompt word planning (such as chain, hierarchical, dynamic, static structure) defines the role and goal of LLM (what it is); stipulates the working method and information processing flow (how it works); and determines the output standard and user experience (what results are delivered and how the experience is).

【Thinking 10】 System prompts are the foundation of the value creation of Agent product managers for a long time. They are also the starting point of AI Agent interaction design. It is like a person buying a house. No matter how he communicates with the homeowner, it must be implemented in the contract. The system prompt is the contract between the user and the LLM .

【Thinking 11】 The work of mainstream agent frameworks (such as LangChain, OpenAI SDK) seems to be co-working with models, but in fact it is about efficient assembly and management of prompt words .

The model is fixed, and the prompt words and their arrangement are the key variables of agent differentiation, effectiveness and value. The same agent framework, different prompt word schemes, may have very different performances. Workflow/chain (flow/chain) is equivalent to the work of serving the organization and adjustment of prompt words.

(Throwing out some ideas)