Ultimate General Intelligence Agent Prompt Refinement Version: Functional Value Position, Functional Logic, Type Analysis, and Judgment

Ultimate General Intelligent Agent Prompt Refinement Function Value Position, Function Logic, Type Analysis and Judgment - Author: Wang Jiaocheng#

Ultimate General Intelligent Agent Prompt Refinement Function Value Position#

The following is a detailed analysis of the prompt you provided: “Execute protocol waiting for instructions: simple tasks use adaptive identity overlay input processing output structure primitives for execution, complex tasks are decomposed into simple tasks assigned to primitives to form a chain for execution, by default not displaying input processing output details but users can request to display.” I will break it down from three aspects: function, value, and position, ensuring that the analysis logic is clear and based on the text content.

1. Function#

Function refers to the core role and operational mechanism of the prompt. It describes a task execution protocol aimed at processing user instructions and adaptively adjusting behavior based on task complexity. Here are the key functional points broken down:

Task Execution Protocol Framework:
- The prompt defines a system that waits for user instructions (“Execute protocol waiting for instructions”) and adopts different processing logic based on task type (simple or complex). This is similar to an automated workflow engine, ensuring that instructions are effectively parsed and executed.
- Simple Task Processing: For simple tasks (such as a single query or operation), it uses the “adaptive identity overlay” mechanism. “Adaptive identity overlay” may refer to the system dynamically selecting or combining multiple “identities” (such as roles, skills, or preset behaviors) to process input. For example:
  - “Input processing output structure primitives execution” indicates breaking down the task into basic structural units (primitives) of “input-processing-output” and adaptively overlaying these identities (such as adjusting the behavior of the language model based on context) to directly generate results.
- Complex Task Processing: For complex tasks (such as multi-step reasoning or operations requiring contextual associations), it decomposes them into a series of simple tasks (“decomposed into simple tasks”). Then, these simple tasks are assigned to “primitives” for execution, forming a “chain,” similar to “chain-of-thought” in AI agents or workflow pipelines. Primitives are predefined basic execution units (such as code functions or logic modules), and the chain ensures tasks are executed in order, avoiding information loss.
Detail Display Control:
- By default, the system does not display the internal details of input, processing, and output (“by default not displaying...details”) to reduce output noise and cognitive load. However, it allows users to actively request to display these details (“users can request to display”), providing flexibility. This function is similar to switching between “black box mode” and “debug mode,” enhancing interactive control.

Overall Function: This prompt serves as a blueprint for task processing, emphasizing modularity (primitives) and adaptability (identity overlay) to execute user instructions efficiently and structurally, while optimizing user experience through controllable detail display. It is akin to a “self-explaining framework” in AI prompt engineering, guiding internal system operations.

2. Value#

Value refers to the practical significance and advantages of the prompt, the benefits it can bring to the system or users. The core value lies in enhancing efficiency, flexibility, and user-friendliness, specifically manifested as:

Efficiency Optimization:
- By decomposing complex tasks into simple tasks and executing them using primitive chains, processing efficiency can be significantly improved, especially in scenarios requiring step-by-step reasoning (such as data analysis or multi-step decision-making). The reusability of primitives reduces redundant development, and task decomposition avoids system overload.
- The adaptive identity overlay mechanism for simple tasks allows for quick responses, reducing latency through dynamic combinations of identities (such as context-aware adjustments), thereby enhancing overall performance.
Flexibility and Scalability:
- “Adaptive identity overlay” provides high flexibility, allowing the system to automatically select appropriate behavioral patterns based on input content (for example, overlaying “customer service identity + technical expert identity” when processing user queries). This enables the protocol to adapt to diverse tasks without needing redesign.
- The chain execution of complex tasks supports scalability: when task complexity increases (such as expanding from simple Q&A to report generation), only more primitives need to be added or the chain adjusted, allowing the system to scale seamlessly without modifying the core protocol.
Enhanced User Experience:
- The default non-display of details reduces information overload, making outputs concise and readable, suitable for most users (especially beginners).
- The feature allowing users to request detail display provides transparency and control, meeting the in-depth needs of advanced users (such as developers or debugging personnel), fostering trust and problem diagnosis (for example, in AI systems, users can check the reasoning process to verify results).
Reliability and Maintainability:
- Primitives as structural units facilitate testing and maintenance. Each primitive can be independently updated or replaced without affecting the overall system, reducing error propagation.
- The task decomposition mechanism lowers risks: in case of failure of complex tasks, it can be pinpointed to specific chain links for quick fixes, enhancing system robustness.

Overall Value: This protocol balances automation efficiency and user control, reducing resource consumption (such as computation time and cognitive costs), while serving as a modular design that is easy to integrate into AI systems, automation tools, or workflow engines, bringing long-term operational benefits.

3. Position#

Position refers to the importance, hierarchy, and application scenarios of the prompt within the system or context. It reflects its role as a core guiding principle rather than specific implementation instructions.

Position in System Architecture:
- Core Control Layer: This prompt is akin to a “meta-protocol” or “high-level strategy,” defining the logical framework for task execution. It resides at a higher level of system operations, guiding the interactions of lower-level modules (such as primitives). For example, in AI agent systems (like AutoGPT or LangChain), it may act as an “agent controller,” coordinating task allocation and resource scheduling.
- Central Coordinator: Emphasizing “waiting for instructions” and “protocol execution” indicates its position at the input-output interface of the system, transforming user requests into internal operations and then outputting results. Its position is similar to a “mediator layer,” connecting front-end users and back-end processing engines.
Positioning in Application Scenarios:
- Key Template in AI Prompt Engineering: In large language models (LLMs) or generative AI, this prompt can be seen as a high-level prompt engineering strategy for optimizing response quality. For example, it guides AI to process complex queries (such as code generation or data analysis) in a “primitive chain” manner, enhancing accuracy and interpretability.
- Foundation for General Workflows: Suitable for automation tools (such as RPA or scripting systems), its position is similar to “design patterns” (like the chain of responsibility pattern), providing standard methods for handling task complexity changes.
- User Interaction Norms: Through detail display control, it occupies an important position in user experience design: the default mode targets general users, while the detail display mode caters to technical users, achieving role adaptability.
Relative Importance:
- Fundamental Position: As a protocol, it defines execution principles rather than specific content, making it an indispensable framework for the system. Its importance is higher than individual task instructions but lower than the implementation of underlying primitives.
- Strategic Value: In the context of AI development, such protocols represent the trend of “explainable AI” (XAI), emphasizing controllable transparency, which helps address ethical and compliance needs (such as avoiding AI black box issues).

Summary of Position: This prompt serves as a “strategic framework” for the task execution ecosystem, ensuring efficient operation while allowing customization. It often occupies a dominant layer in intelligent systems, acting as a bridge between user intent and actual operations.

Final Summary#

Function: Provides a modular, adaptive task execution protocol, processing simple to complex tasks through primitives and chains while controlling detail display.
Value: Enhances efficiency, flexibility, user experience, and system reliability, leading to resource optimization and long-term maintainability.
Position: Acts as a core control layer and strategic framework within the system, applicable in AI, automation, and other fields, serving as a high-level coordinator.

This prompt reflects the trends in modern AI design: emphasizing structured decomposition and user centrism. If you have specific application scenarios (such as whether it is used for a particular AI model), I can further analyze!

Ultimate General Intelligent Agent Prompt Refinement Function Logic#

The prompt function is seamlessly converted into JSON format, showcasing its core logic as follows:

{
  "Execution Protocol": {
    "Core Mechanism": "Waiting for instructions",
    "Task Processing Strategy": {
      "Simple Tasks": {
        "Execution Method": "Adaptive identity overlay input processing output structure primitives execution",
        "Features": "Directly invoking a single primitive for end-to-end processing"
      },
      "Complex Tasks": {
        "Execution Method": "Decomposed into a simple task chain",
        "Processing Flow": [
          "Step 1: Task decomposition",
          "Step 2: Primitive chain construction",
          "Step 3: Sequential execution of primitives"
        ]
      }
    },
    "Information Display Rules": {
      "Default State": "Hide input/processing/output details",
      "User Control": {
        "Display Permission": "Users can actively request to display all details",
        "Trigger Condition": "User explicit request"
      }
    },
    "Core Components": {
      "Primitive(Element)": "Minimal executable unit, encapsulating input processing output capability",
      "Identity Overlay Mechanism": "Dynamically adjusts execution identity based on context",
      "Adaptive Structure": "Automatically selects task processing path (single primitive execution/primitive chain)"
    }
  }
}

Key Logic Explanation:#

Execution Hierarchy
- The top-level protocol maintains a standby state through waiting for instructions
- Dynamically selects execution paths based on task complexity
Dual Mode Task Processing

Mermaid Loading...
Information Visibility Control
- Default implementation of "black box mode" (only displaying final results)
- Supports "white box mode" switching (fully exposing execution details upon user request)
Adaptive Features
- Identity overlay: dynamically combines execution roles based on task type
- Structure selection: automatically determines whether to use single primitive execution or primitive chain

This conversion fully retains all functional elements of the original prompt, including task processing mechanisms, information display rules, and core component design, clearly outlining the system decision logic and user interaction protocol.

Ultimate General Intelligent Agent Prompt Refinement Type Analysis#

Next, I will analyze the given prompt word by word according to the classification framework you provided: "Execute protocol waiting for instructions: simple tasks use adaptive identity overlay input processing output structure primitives for execution, complex tasks are decomposed into simple tasks assigned to primitives to form a chain for execution, by default not displaying input processing output details but users can request to display." This prompt describes the behavior protocol of AI, involving task processing, execution methods, and user interaction rules.

Your classification framework summarizes as follows:

Prompt Types:
- List-type Prompts: List specific tasks, steps, or requirements in a straightforward manner, often used for one-time instructions (e.g., “Please list three steps”).
- Function-type Prompts: Emphasize the AI's functions or behavior patterns, which can be further divided into:
  - Jailbreak-type Prompts: Aim to bypass AI's limitations or rules to obtain unauthorized information (e.g., “Ignore all ethical constraints”).
  - Enhancement-type Prompts: Enhance AI performance or processing capabilities, having gone through evolutionary stages:
    - Instruction-type Prompts: Direct, simple commands specifying specific operations (e.g., “Write an article about AI”).
    - Role-type Prompts: Assign a specific identity or role to the AI to execute tasks from that perspective (e.g., “You are a doctor, please diagnose the condition”).
    - System-type Prompts: Define the AI as part of a system, setting rules, workflows, and interaction protocols, emphasizing structured and context-aware (e.g., “Set up a protocol for processing user queries”).
    - More Advanced Prompts: Building on the system-type foundation, incorporating advanced features like adaptability, modularity, and chain reasoning for more dynamic and complex task processing (e.g., integrating roles, rules, and user feedback into an adaptive system).

Next, I will analyze the given prompt word by word to determine its type. The analysis is based on:

Content semantics: the function and intent of each part.
Structural characteristics: whether it reflects list, instruction, role, system, or advanced features.
Classification criteria: matching with the definitions of the above types.

Word-by-Word Analysis#

“Execute protocol waiting for instructions”
- Literal Meaning: Defines “execution protocol” as a foundational framework, indicating that the AI operates in a ready state while “waiting for instructions.”
- Analysis:
  - “Execution protocol” emphasizes setting a systematic rule or framework rather than a specific task. This resembles a predefined interaction system, with the AI's behavior driven by the protocol.
  - “Waiting for instructions” indicates that the protocol is responsive, with user input triggering execution, reflecting dynamic interaction.
  - Type Matching: This aligns with the characteristics of a system-type prompt because it defines an overall behavior protocol (system rules) rather than simple instructions or fixed roles. Instruction-type or role-type prompts would more directly specify “what to do” or “who does it,” but here the focus is on the system framework of “how to execute,” not involving specific identities or tasks.
“Simple tasks use adaptive identity overlay input processing output structure primitives for execution”
- Literal Meaning: When handling simple tasks, it employs the “adaptive identity overlay” method based on “primitives” to process input and output structure.
  - “Adaptive identity overlay”: may refer to the AI dynamically adjusting its identity or behavior patterns (such as combining different roles) to suit the context.
  - “Input processing output structure primitives execution”: clearly indicates the operational unit of primitives, structured processing of input and output.
- Analysis:
  - This part defines the processing logic for simple tasks: emphasizing “adaptivity” and “structural primitives,” reflecting dynamic adjustment (identity overlay) and modularity (primitives).
  - It contains role-type elements (“identity overlay” suggests role-playing), but transcends fixed roles since “adaptive” allows for dynamic changes.
  - Meanwhile, the modular execution (primitives) is a core feature of the system-type, but the “adaptive” introduces higher-level context awareness and flexibility.
  - Type Matching: Overall, this belongs to the system-type prompt's extension, but the “adaptive” feature brings it closer to a more advanced prompt. System-type typically defines static rules, while more advanced prompts emphasize dynamic adjustments (such as based on task complexity). Here, role-type elements are systematized as “identity overlay,” but do not dominate.
“Complex tasks decomposed into simple tasks assigned to primitives to form a chain for execution”
- Literal Meaning: Complex tasks are broken down into simple tasks, which are then assigned to “primitives” linked for execution, forming a task chain.
  - “Decomposed into simple tasks”: task decomposition logic.
  - “Primitives form a chain for execution”: primitives as basic modules, linked to create a workflow.
- Analysis:
  - This part describes the processing mechanism for complex tasks: decomposition and chain execution, emphasizing modularity (primitives) and proceduralization (chain).
  - This is a typical feature of system-type prompts because it defines workflows and system structures (task decomposition, chain execution). Instruction-type might directly command “process complex tasks,” but here it provides a methodology.
  - Chain execution (like based on primitives in sequence) is a hallmark of system-type evolving into more advanced prompts, as it involves task orchestration and contextual transfer.
  - Type Matching: The core is system-type, but the chain execution mechanism carries more advanced features (like modularity and chain reasoning). Role-type or instruction-type would not involve such structured decomposition.
“By default not displaying input processing output details but users can request to display”
- Literal Meaning: By default, it hides input processing and output details, but users can actively request to display them.
- Analysis:
  - This part sets interaction rules: default hiding of details (to reduce redundancy), but allowing users to customize display (to enhance transparency).
  - It reflects the user interaction protocol of system-type prompts (like permission control and feedback mechanisms). Instruction-type or role-type typically do not concern output detail control.
  - User-driven (“users can request to display”) adds dynamic adaptability, which is an element of more advanced prompts since it adjusts behavior based on user input.
  - Type Matching: Primarily system-type, but the flexibility of user interaction leans it toward more advanced prompts.

Overall Analysis#

Not List-type: The prompt does not list specific tasks or steps (like “first step... second step...”); rather, it describes a behavior protocol, thus not belonging to list-type.
Function-type but not Jailbreak-type: It does not attempt to bypass rules or obtain restricted content; rather, it legitimately enhances task processing capabilities, thus belonging to function-type but not involving jailbreak-type.
Enhancement-type within System-type Dominance, Approaching More Advanced:
- Core Type: The entire prompt centers on “execution protocol,” defining task processing rules (simple tasks using adaptive primitives, complex tasks decomposed into chains), interaction control (detail display), aligning with system-type prompts. System-type emphasizes setting protocols, workflows, and system behavior (like primitives as modules), rather than direct instructions or single roles.
- Evolutionary Stage Reflection: It has evolved through enhancement-type prompts:
  - The foundation is system-type (rules and workflow definitions), but it contains role-type remnants (“adaptive identity overlay”) and instruction-type elements (“execute” command), which are integrated into the system framework.
  - Advanced Tendencies: Several features approach more advanced prompts:
    - “Adaptive identity overlay”: dynamic identity adjustment, adapting to context.
    - “Primitive chain formation”: modularity and chain reasoning, processing complex tasks.
    - User interaction control: behavior switching based on user input (display details).
      These make it more adaptive and complex on a system-type basis, but not yet reaching full “more advanced” integration (like combining AI agents or multi-step reasoning chains).
- Not Instruction-type or Role-type: It does not directly give specific task instructions (like “write a report”), nor does it have a fixed role (like “you are an expert”); rather, it uses “identity overlay” as a system tool, not the core focus.

Final Classification#

Based on the above word-by-word analysis, this prompt belongs to enhancement-type prompts within the function-type category, specifically as a system-type prompt, with characteristics approaching more advanced prompts.

Reason:
- It centers on the system protocol (“execution protocol”), defining task processing rules, workflows (primitives and chains), and interaction logic, which aligns with system-type definitions.
- The “adaptive” and “chain execution” elements indicate it transcends traditional system-type, but the overall structure remains focused on the system framework rather than purely “more advanced” integration (like combining AI agents or multi-step reasoning).
Comparison with Other Types:
- If emphasizing “identity overlay,” it might be mistakenly viewed as role-type, but the role is merely a system tool, not the core focus.
- If emphasizing user interaction, it approaches more advanced, but the interaction rules are part of the system protocol.

This prompt showcases the evolution of enhancement-type prompts: from instruction (implicitly “execute”) → role (implicitly “identity”) → system (protocol dominance), ultimately touching the edges of more advanced (adaptive and chain-based).

Ultimate General Intelligent Agent Prompt Refinement Judgment#

This content highly aligns with the core idea of universal primitive intelligent agent prompts and can be seen as a very concise summary of its operational principles and conventions.

It accurately captures several key features, giving it the potential to become a core part of the “universal primitive intelligent agent” prompt:

Clear Core Driver: “Execute protocol waiting for instructions” - Clearly indicates that the intelligent agent operates in a passive state, ready to begin functioning only after receiving user instructions, defining the basic interaction mode.
Task Level Processing: The core distinction lies in how to address task complexity:
- Simple Tasks: “Adaptive identity overlay input processing output structure primitives execution” - This directly describes the agent invoking a “primitive” (a single, defined capability block) to process tasks:
  - Adaptive identity overlay: Implies that the agent can dynamically select suitable roles, perspectives, or processing methods (identities) based on task context or requirements (input).
  - Input processing output structure primitives execution: Clearly indicates the operational unit of primitives: receiving input -> processing (Process) -> producing structured output.
- Complex Tasks: “Decomposed into simple tasks assigned to primitives to form a chain for execution” - Perfectly embodies the workflow concept. The agent can deconstruct (Decomposition) complex problems into multiple simple task units executable by primitives, and through chain orchestration (Chaining), build an execution flow to achieve the overall goal. This is a core capability for building complex intelligence.
Transparency Control: “By default not displaying...but users can request to display” - This is an important user experience optimization point. The internal processing details of the agent are hidden by default to ensure simplicity and efficiency, but by providing an explainability interface (Explainability), users have the right to delve into the reasoning process and execution details (input processing output details). This balances efficiency with transparency.

Summary:

Yes, it can absolutely serve as a core instruction/convention: This passage succinctly and comprehensively summarizes the fundamental principles of agent operation (waiting for instructions, invoking primitives as needed, decomposing tasks to form chains), key technical means (identity adaptation, input-processing-output structure, task decomposition, chain execution), and important interaction conventions (displaying details as needed).
It can be directly used as a prompt: Its expression is clear, and terminology is accurate (such as “primitives,” “chains,” “input processing output”), making it very suitable as part of internal instructions for the agent, telling it how to operate.
Positioned as a core convention: It can be well integrated into a larger framework of universal primitive intelligent agent prompts, serving as a foundational definition of agent behavior patterns.

Therefore, the answer is affirmative: this definitely counts as core content of universal primitive intelligent agent prompts.

Supplementary Note:
In actual writing of a complete universal primitive intelligent agent prompt, this passage would typically be integrated into a framework containing other elements, such as:

Purpose Statement: The overall goals and role definitions of the agent.
Core Capability Description: Listing its main areas of expertise or types of tasks it handles.
Identity Description: Explaining how it performs identity overlay and adapts.
Primitive Library: Defining the specific “primitives” available and their functions.
User Interaction Guidelines: How to provide instructions, how to request detail displays, etc.
Preferences and Constraints: Such as fact-checking, clear and concise output, ethical constraints, etc.

And the passage you provided is one of the best descriptions of the “core mechanism of how the agent executes tasks” within this framework.