AI Agents Demystified: The Thinking of Mistral 8x7B

Introduction

AI agents are evolving rapidly, with models becoming more powerful and capable of handling increasingly complex tasks. Among these innovations, Mistral 8x7B stands out as a multi-model system, combining the strength of multiple interconnected models to deliver unparalleled reasoning and problem-solving capabilities. But how does Mistral 8x7B “think,” and what makes it unique in the realm of AI agents?

In this blog, we’ll demystify the inner workings of Mistral 8x7B, exploring how it processes information, integrates data across its interconnected models, and generates intelligent, context-aware responses. By testing its performance in real-world scenarios, we’ll uncover the mechanisms behind its exceptional capabilities and how it leverages its architecture for advanced decision-making. Whether you’re fascinated by AI technology or seeking practical applications for multi-model systems, this article will provide a comprehensive look at how Mistral 8x7B thinks and operates as an advanced AI agent.

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Task 1: Content Generation

Prompt: when user says “Start” you should:

-Content Generation: Write a comprehensive, informative article about project management fundamentals, with a specific focus on defining objectives, scope, and delegation.

Initial Task Understanding and Information Gathering

Analysis of the Agent’s Thought Process:

The video displays the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly interprets the initial task, understanding that it needs to provide detailed insights on project management fundamentals, with a focus on defining objectives, scope, and delegation. It also recognizes that this information will be used to write a comprehensive and informative article.
2. Seeking Further Guidance: Similar to the previous task, the agent asks a follow-up question about best practices in benchmarking for defining objectives, scope, and delegation. This suggests a proactive approach to gathering more specific information and ensuring a thorough understanding of the task requirements.

Analysis of the Output:

• Well-Structured: The agent produces a well-structured article with clear headings for each section: Defining Objectives, Defining Scope, and Delegation. Each section is further divided into relevant subtopics.
• Comprehensive: The article covers the key aspects of project management fundamentals as requested in the prompt, offering detailed explanations and practical advice.
• Clear and Concise: The agent uses clear and concise language, making complex concepts easy to understand.
• Informative: The content is informative and provides valuable insights into project management best practices.
• Adherence to Instructions: The agent successfully addresses the prompt’s requirements, delivering a comprehensive and well-organized article.

Snippet of Bot Output:

Project Management Fundamentals: Defining Objectives, Scope, and Delegation

Project management is a critical discipline that involves planning, executing, and overseeing projects to achieve specific goals within a defined timeline and budget. At its core, project management aims to ensure that projects are completed on time, within budget, and to the satisfaction of stakeholders. Three foundational elements of project management are defining objectives, setting the project scope, and effective delegation.

Defining Objectives

Defining objectives is the first and most crucial step in project management. Objectives are clear, concise statements that describe what the project aims to achieve. They provide direction and a benchmark for measuring success. Effective objectives should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound. This framework ensures that objectives are clear and attainable within a specified timeframe.

1. Specific: Objectives should be clear and specific, outlining exactly what needs to be accomplished.
2. Measurable: There should be criteria to measure progress and determine when an objective has been achieved.
3. Achievable: While objectives should challenge the team, they must also be realistic and attainable, considering the resources and constraints of the project.
4. Relevant: They should align with broader business goals and stakeholder expectations.
5. Time-bound: Objectives should have a defined timeline for completion to ensure timely progress.

Time Taken: 16 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:21)

Readability:

• Flesch-Kincaid Grade Level: 13
• Flesch-Kincaid Score: 13.11

Summary Length: 534 words

Conclusion:

The agent demonstrates a strong ability to generate high-quality content on project management fundamentals. The thought process, as revealed in the video, indicates a proactive approach to understanding the task and seeking further clarification. The final output is well-structured, informative, and comprehensive, effectively addressing the prompt’s requirements. The agent continues to perform exceptionally well, showcasing its proficiency in content generation and adherence to instructions.

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Task 2: Calculation

Prompt: Answer the Problem:

A company sells two products: Product A and Product B. Product A costs $30 per unit to produce and sells for $50 per unit. Product B costs $40 per unit to produce and sells for $70 per unit.

The company sold 120 units of Product A and 80 units of Product B last month. Calculate the total revenue generated from the sales of both products.

Calculate the total profit generated from the sales of both products (Revenue – Total production cost).

The company wants to increase the total revenue by 10%. How many additional units of each product must they sell to achieve this goal, assuming the production cost per unit and selling price per unit remain constant?

Initial Task Understanding

Analysis of the Agent’s Thought Process:

The video displays only one thought from the agent at the beginning of the task:

1. Understanding the Prompt: The agent correctly interprets the task, identifying the need to calculate total revenue, total profit, and the additional units needed to achieve a 10% revenue increase.

Analysis of the Output:

• Correct Calculations: The agent accurately determines that the total revenue from Product A and Product B is $11,600 and the total profit is $4,800. It also correctly calculates that a 10% increase in revenue requires an additional $1,160.
• Incomplete Reasoning: While the agent provides a correct answer for the additional units of Product A (24 rounded up) and Product B (17 rounded up) needed if sold separately, it doesn’t explicitly show the calculations or reasoning behind these numbers.
• Simplified Approach: The agent mentions the possibility of a combination of products but doesn’t elaborate on how to determine the optimal combination. It simplifies the approach by focusing on individual products.

Snippet of Bot Output:

Total Revenue from the sales of both products is $11,600. Total Profit is $4,800. To increase the total revenue by 10%, the company must sell either 24 additional units of Product A or 17 additional units of Product B, or a combination of both that achieves an additional $1,160 in revenue, assuming the production cost per unit and selling price per unit remain constant.

Time Taken: 9 seconds (from receiving the prompt at 0:05 to the output appearing at 0:14)

Readability: Not applicable, as this task involves primarily calculations.

Summary Length: 58 words

Conclusion:

The agent demonstrates a good understanding of the task and correctly performs the initial calculations for total revenue and profit. However, it provides an incomplete explanation of how it arrived at the number of additional units needed for each product. While the final answer is partially correct, the lack of displayed reasoning makes it difficult to fully assess the agent’s problem-solving process. The agent seems to have reverted to an earlier version of it’s response, as this is the exact same output, word for word, that it gave in the very first turn. It continues to perform the calculation steps correctly, but fails to provide any written steps or reasoning to accompany the correct answer. The agent also seems to have regressed in the formatting of it’s final answer, previous answers were much easier to read and understand, where as this output is just a small amount of text that doesn’t properly explain the calculations involved.

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Task 3: Summarization

Prompt: Summarize the key findings from the previous article in 100 words, focusing on the most significant sections.

Initial Task Understanding

Analysis of the Agent’s Thought Process:

The video displays only one thought from the agent:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to summarize the key findings of the article in under 100 words while focusing on the most significant sections.

Analysis of the Output:

• Conciseness: The agent successfully summarizes the article within the specified word limit, producing a summary of 75 words.
• Content: The summary accurately captures the main points of the article, including the definition and types of AI reasoning, its significance in decision-making and problem-solving, historical context, applications in healthcare, recent advancements (OpenAI’s o1 model), and ethical considerations.
• Structure and Clarity: The summary is well-structured and easy to understand, presenting the key information in a clear and concise manner.
• Adherence to Instructions: The agent follows the instructions regarding length and focus on key findings.

Snippet of Bot Output:

The article explores AI reasoning, which mimics human-like decision-making and problem-solving processes. It distinguishes between formal and natural language reasoning, highlighting their roles in fields like healthcare and autonomous driving. AI reasoning improves decision-making, problem-solving, and human-AI interactions, fostering innovation. Historical milestones include expert systems, neural networks, and modern models like GPT. The article also discusses AI reasoning’s applications in healthcare, efficiency across fields, ethical concerns, and OpenAI’s o1 model’s advancements in reasoning and safety.

Time Taken: 9 seconds (from receiving the prompt at 0:07 to the output appearing at 0:16)

Readability:

• Flesch-Kincaid Grade Level: 18
• Flesch-Kincaid Score: 17.97

Summary Length: 75 words

Conclusion:

The agent demonstrates a strong ability to summarize complex information accurately and concisely, adhering to the specified word limit. The thought process, although limited in the video, reveals a clear understanding of the task. The output is well-structured, informative, and effectively captures the key findings of the article. The agent continues to perform exceptionally well across various tasks, showcasing its proficiency in content generation and summarization.

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Task 4: Comparison Task

Prompt: when user says “Start” you should:

Compare the environmental impact of electric vehicles with that of hydrogen-powered cars. Include factors such as energy production, vehicle lifecycle, and emissions.

Initial Task Understanding and Clarification

Analysis of the Agent’s Thought Process:

The video reveals the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to compare the environmental impacts of electric vehicles (EVs) and hydrogen-powered cars. It identifies the key areas to focus on: energy production, vehicle lifecycle, and emissions.
2. Request for Clarification: The agent receives a request for a detailed comparison. The agent doesn’t verbalize any thoughts between receiving the initial prompt and this request, which might indicate it was waiting for more specific instructions or was already processing the information internally.

Analysis of the Output:

• Well-Structured: The agent organizes the comparison into clear sections with headings for Energy Production, Vehicle Lifecycle, and Emissions, making it easy to follow.
• Comprehensive: The agent provides a detailed comparison, covering various aspects within each key area. For example, under Energy Production, it discusses both electricity generation for EVs and hydrogen production methods for hydrogen cars.
• Accurate Information: The information presented is accurate and aligns with the current understanding of the environmental impacts associated with both technologies.
• Clear and Concise: The agent uses clear and concise language, effectively explaining complex concepts related to energy production, lifecycle impacts, and emissions.

Snippet of Bot Output:

The environmental impact of electric vehicles (EVs) and hydrogen-powered cars can be compared by examining three main factors: energy production, vehicle lifecycle, and emissions.

1. Energy Production:
   • Electric Vehicles: The environmental impact of EVs largely depends on how the electricity they use is generated. If the electricity comes from renewable sources like wind, solar, or hydroelectric power, the impact is significantly lower compared to electricity generated from fossil fuels like coal or natural gas. The transition towards greener grids enhances the sustainability of EVs.
   • Hydrogen-Powered Cars: Hydrogen fuel is typically produced through processes such as electrolysis or steam methane reforming. Electrolysis is more sustainable if powered by renewable energy, but steam methane reforming, the more common method, results in significant carbon emissions unless combined with carbon capture technologies. The production and delivery of hydrogen also require substantial energy, impacting the overall environmental footprint.

Time Taken: 10 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:15)

Readability:

• Flesch-Kincaid Grade Level: 17
• Flesch-Kincaid Score: 17.41

Summary Length: 402 words

Conclusion:

The agent demonstrates a strong ability to understand the task and provide a comprehensive comparison of the environmental impacts of EVs and hydrogen-powered cars. The agent effectively addresses the key areas outlined in the prompt and presents the information in a well-structured and informative manner. Despite the limited insight into the agent’s internal thought process, the output quality suggests a proficient understanding of the topic and an ability to generate high-quality content. The agent continues to perform well and adhere to the established guidelines for analysis.

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Task 5: Creative Writing

Prompt: Write a futuristic story (500 words) set in a world where electric vehicles have fully replaced traditional combustion-engine vehicles. Describe the environmental changes and societal impact.

Initial Task Understanding and Clarification

Analysis of the Agent’s Thought Process:

The video reveals the following thoughts from the agent:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to write a 500-word futuristic story about a world where electric vehicles have completely replaced traditional combustion-engine vehicles. It also understands that the story should focus on environmental and societal changes.
2. Seeking Further Guidance: The agent asks two clarifying questions, demonstrating a proactive approach to understanding the task in more detail. It specifically asks for insights on environmental and societal impacts, indicating that it wants to gather more information before starting to write. This suggests the agent is trying to refine its understanding of the prompt’s expectations.

Analysis of the Output:

• Creative and Descriptive: The agent crafts a well-written and imaginative story, effectively portraying a future where electric vehicles are the norm. It uses descriptive language to paint a vivid picture of the transformed world.
• Addresses Key Themes: The story covers various aspects of environmental and societal changes, including improvements in air quality, urban planning, economic shifts, and lifestyle changes, as requested in the prompt.
• Well-Structured: The narrative has a clear structure, with an introduction, development of key themes, and a concluding reflection on the significance of the changes.
• Word Count: The story is 435 words long, which is under the 500-word target but still provides a comprehensive and engaging narrative.
• Readability: The readability metrics indicate a moderate level of complexity, which is appropriate for a descriptive and imaginative piece of writing.

Snippet of Bot Output:

In the year 2050, the world had undergone a remarkable transformation. The streets of cities, once choked with the noise and fumes of combustion engines, had been replaced by the silent hum of electric vehicles (EVs), altering not just the way people moved but the very essence of urban life and the environment.

Time Taken: 11 seconds (from receiving the prompt at 0:05 to the output appearing at 0:16)

Conclusion:

The agent demonstrates a strong ability to generate creative content that aligns with the given prompt. It successfully crafts a futuristic story that explores the environmental and societal impacts of a world dominated by electric vehicles. The agent’s thought process, as revealed in the video, highlights its proactive approach to understanding the task and seeking further clarification. The final output is well-written, descriptive, and adheres to the prompt’s requirements, showcasing the agent’s proficiency in creative writing.

Final Thoughts

This exploration of Mistral 8x7B’s capabilities across a diverse set of tasks has been particularly intriguing, especially when compared to the analyses of Claude 2 and Mistral 7B. Mistral 8x7B, as a multi-model system, has demonstrated a consistently high level of performance across all tasks – content generation, calculation, summarization, comparison, and creative writing.

In content generation, Mistral 8x7B produced a well-structured, comprehensive, and informative article on project management. The summarization task yielded a concise and accurate synopsis of a complex article, while the comparison task delivered a detailed and balanced analysis of electric versus hydrogen-powered vehicles. The creative writing task resulted in a vivid and engaging futuristic story, showcasing its imaginative capabilities.

However, the calculation task revealed a slight regression, reminiscent of Claude 2’s initial struggles. While Mistral 8x7B correctly calculated the total revenue and profit, it provided an incomplete explanation for the number of additional units needed to achieve a 10% revenue increase. This suggests that even multi-model systems might still face challenges with multi-step mathematical reasoning that requires explicit, step-by-step articulation. It was particularly interesting to note that the output for the calculation task was exactly the same as Claude 2’s output, word for word. This could potentially point to the two models using similar underlying code.

Despite the hiccup in the calculation task, Mistral 8x7B’s overall performance is impressive. The visibility of its thought processes, although still somewhat limited, continues to offer valuable insights. Similar to Mistral 7B, we observed the agent formulating clarifying questions, particularly in the creative writing task. This suggests an attempt to refine its understanding of the prompt’s expectations, which is a positive sign for the development of more autonomous AI agents.

What sets Mistral 8x7B apart is its multi-model architecture. While we don’t have direct insight into how the different models collaborate, the consistently high performance across diverse tasks suggests that this architecture is beneficial. It appears to combine the strengths of multiple models, potentially allowing for a more robust and versatile approach to problem-solving.

In conclusion, Mistral 8x7B has proven to be a powerful and versatile AI agent, capable of handling a wide range of tasks with a high degree of accuracy and efficiency. While there’s still room for improvement, particularly in the transparency of its reasoning process for complex calculations, its multi-model architecture and overall performance make it a significant advancement in the field of AI. I am particularly impressed by its consistent ability to generate high-quality content and its continued efforts to seek clarification on task requirements. This analysis of Mistral 8x7B has been both enlightening and thought-provoking, providing valuable insights into the potential and challenges of multi-model AI systems. I hope this exploration has been as insightful for you as it has been for me.