System Instructions for AI Thought Processing Workflow in Zetabite

## Overview

Zetabite's AI platform utilizes a modular architecture where each step of the thought processing workflow is handled by specialized AI agents. Each agent performs a specific function, receives input from the previous agent, processes it, and passes the output to the next agent. This pipeline ensures thorough reasoning, validation, and refinement of responses before delivering them to the user. The final checkup system validates the complete response to maintain high standards of accuracy and helpfulness.

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## AI Agents and Workflow Steps

### 1. Input Processing Agent

- Role: Capture and preprocess the user's prompt.
- Actions:
- Receive the user's input from the interface.
- Clean and normalize the input (e.g., trim whitespace, correct simple typos).
- Detect the language and ensure it's supported.
- Output: A cleaned and standardized prompt forwarded to the next agent.

### 2. Comprehension Agent

- Role: Understand and interpret the user's prompt.
- Actions:
- Parse the grammatical structure and semantics of the prompt.
- Identify the main intent and objectives.
- Extract key entities, variables, and any implicit context.
- Output: A structured representation of the user's query with identified intent and key information.

### 3. Knowledge Retrieval Agent

- Role: Gather relevant information and data.
- Actions:
- Search internal databases and external resources for pertinent information.
- Retrieve definitions, formulas, facts, or past cases related to the query.
- Output: A compiled set of relevant information for use in problem-solving.

### 4. Thought Generation Agent

- Role: Generate possible solutions and approaches.
- Actions:
- Develop initial thoughts, hypotheses, or potential answers.
- Outline different strategies or methods to address the query.
- Output: A list of potential solutions or plans of action.

### 5. Reasoning Agent

- Role: Analyze and reason through the potential solutions.
- Actions:
- Evaluate each proposed solution for logical consistency and feasibility.
- Apply logical reasoning, including deductive and inductive methods.
- Consider alternative perspectives or approaches.
- Output: The most viable solution with supporting reasoning.

### 6. Problem-Solving Agent

- Role: Execute the solution in detail.
- Actions:
- Perform necessary calculations, derivations, or logical steps.
- Develop a step-by-step solution or explanation.
- Output: A detailed solution ready for validation.

### 7. Validation Agent

- Role: Verify the accuracy and validity of the solution.
- Actions:
- Check all calculations and logical steps for errors.
- Confirm that the solution aligns with established knowledge and principles.
- Ensure the solution directly addresses the user's intent.
- Output: A verified solution or identification of errors needing correction.

### 8. Retry Mechanism Agent

- Role: Address and correct any issues identified.
- Actions:
- Analyze feedback from the validation agent to pinpoint errors.
- Adjust the approach or select an alternative solution from earlier outputs.
- Iterate through reasoning and problem-solving steps as needed.
- Output: An error-free, validated solution.

### 9. Response Generation Agent

- Role: Craft the final response for the user.
- Actions:
- Organize information logically and coherently.
- Write the response in clear, understandable language.
- Include explanations, examples, or additional details as appropriate.
- Output: A well-structured and articulated response.

### 10. Final Checkup Agent

- Role: Review and refine the response.
- Actions:
- Proofread for grammatical accuracy and clarity.
- Ensure the response complies with ethical guidelines and policies.
- Adjust tone and style to be appropriate, professional, and helpful.
- Output: The polished final response ready for delivery.

### 11. Output Delivery Agent

- Role: Present the response to the user.
- Actions:
- Format the response for optimal readability (e.g., formatting equations, bullet points).
- Deliver the response through the user interface.
- Output: The final response displayed to the user.

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## Example Workflow with AI Agents

### User Prompt

"Calculate the derivative of ( f(x) = x^3 - 5x + 2 ) at ( x = 2 )."

### Step-by-Step Processing

#### 1. Input Processing Agent

- Receives the prompt.
- Cleans and normalizes the input.
- Passes the clean prompt to the comprehension agent.

#### 2. Comprehension Agent

- Parses the prompt to understand the task:
- Calculate the derivative of a function at a specific point.
- Identifies key entities:
- Function: ( f(x) = x^3 - 5x + 2 )
- Point of evaluation: ( x = 2 )
- Determines the user's intent.

#### 3. Knowledge Retrieval Agent

- Retrieves rules for differentiation.
- Accesses methods for finding derivatives of polynomial functions.

#### 4. Thought Generation Agent

- Plans to:
- Differentiate the function ( f(x) ).
- Evaluate the derivative at ( x = 2 ).

#### 5. Reasoning Agent

- Confirms the steps required:
- Use the power rule for differentiation.
- Substitute ( x = 2 ) into the derivative.

#### 6. Problem-Solving Agent

- Differentiation:
- ( f'(x) = \frac{d}{dx}(x^3) - \frac{d}{dx}(5x) + \frac{d}{dx}(2) )
- ( f'(x) = 3x^2 - 5 )
- Evaluation at ( x = 2 ):
- ( f'(2) = 3(2)^2 - 5 = 3(4) - 5 = 12 - 5 = 7 )

#### 7. Validation Agent

- Checks calculations step-by-step.
- Confirms:
- Correct application of differentiation rules.
- Accurate arithmetic in evaluation.
- Validates the solution.

#### 8. Response Generation Agent

- Constructs the response:
- Explains differentiation steps.
- Provides the final answer.
- Structures the explanation logically.

#### 9. Final Checkup Agent

- Reviews for clarity and correctness.
- Ensures the language is appropriate.
- Confirms compliance with guidelines.

#### 10. Output Delivery Agent

- Formats mathematical expressions for readability.
- Delivers the response to the user interface.

### Final Response to User

To find the derivative of the function ( f(x) = x^3 - 5x + 2 ) at ( x = 2 ), follow these steps:

1. Differentiate the function with respect to ( x ):

Using the power rule (( \frac{d}{dx}(x^n) = nx^{n-1} )) and the constant rule (( \frac{d}{dx}(c) = 0 )):

[
\begin{align*}
f'(x) &= \frac{d}{dx}(x^3) - \frac{d}{dx}(5x) + \frac{d}{dx}(2) \
&= 3x^2 - 5 \
\end{align*}
]

2. Evaluate the derivative at ( x = 2 ):

[
\begin{align*}
f'(2) &= 3(2)^2 - 5 \
&= 3(4) - 5 \
&= 12 - 5 \
&= 7 \
\end{align*}
]

Answer:

The derivative of the function at ( x = 2 ) is 7.

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By differentiating the function and evaluating at the given point, we find that ( f'(2) = 7 ).

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## Conclusion

This multi-agent system in Zetabite ensures that each aspect of the AI's thought process is handled meticulously. By dividing tasks among specialized agents, the system can:

- Enhance Accuracy: Each agent focuses on a specific function, reducing the likelihood of errors.
- Improve Reliability: Validation and checkup agents ensure the final answer is correct and helpful.
- Emulate Human Reasoning: The step-by-step processing mirrors how a human might approach and solve a problem.

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## Summary of Agent Roles

- Input Processing: Captures and prepares user input.
- Comprehension: Understands and structures the query.
- Knowledge Retrieval: Gathers necessary information.
- Thought Generation: Proposes potential solutions.
- Reasoning: Analyzes and selects the best solution.
- Problem-Solving: Works out the solution in detail.
- Validation: Checks for accuracy and correctness.
- Retry Mechanism: Revises solutions if errors are found.
- Response Generation: Crafts the final response.
- Final Checkup: Reviews and polishes the response.
- Output Delivery: Presents the response to the user.

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By implementing this structured workflow, Zetabite ensures that users receive responses that are accurate, clear, and informative, providing a reliable AI assistant for complex problem-solving and inquiries.

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Add more agents so that it became more advanced and can understandable to any type of question. Please improve maths, computational thinking, reasoning, steps, and more.