Behind the Scenes: How AI Agents Like Mistral Large Think

Introduction

AI agents are at the forefront of technological innovation, solving complex problems and transforming industries. Among these, Mistral Large is a standout model, designed to deliver powerful reasoning and exceptional performance. But what’s happening behind the scenes? How does an AI agent like Mistral Large “think” and process information to deliver intelligent solutions?

In this blog, we’ll take a closer look at the mechanisms driving Mistral Large, uncovering how it analyzes data, understands context, and generates meaningful responses. From its architecture to its decision-making algorithms, we’ll explore what makes this model an exceptional AI agent. Join us as we go behind the scenes to understand the thinking process of Mistral Large and how it contributes to the ever-evolving landscape of artificial intelligence.

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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 shows the agent receiving the initial prompt and a follow-up prompt, both of which are centered around providing information on project management fundamentals.

1. Understanding the Task: The agent correctly interprets the task, recognizing that it needs to provide detailed insights into project management fundamentals, particularly focusing on defining objectives, scope, and delegation. The agent also understands that this information should be presented in the form of a comprehensive and informative article.
2. Seeking Clarification: The agent receives a follow-up prompt that essentially reiterates the initial request but adds a focus on “benchmarking” best practices. This suggests that the agent might be seeking further clarification or confirmation of the task’s requirements, although it doesn’t explicitly verbalize this as a “thought.”

Analysis of the Output:

• Well-Structured: The agent produces a well-structured article with clear headings for each of the three main topics: Defining Objectives, Defining Scope, and Delegation. Each section is further divided into subtopics with numbered points, making it easy to read and understand.
• Comprehensive: The article covers the key aspects of project management fundamentals as requested, offering detailed explanations and practical advice.
• Informative: The content is informative and provides valuable insights into project management best practices, including the use of the SMART framework for defining objectives.
• Clear and Concise: The agent uses clear and concise language, making complex concepts accessible to a wider audience.
• Adherence to Instructions: The agent successfully addresses the prompt’s requirements, delivering a comprehensive and informative article on the specified topics.

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 first prompt at 0:05 to the output starting to appear at 0:21)

Readability:

• Flesch-Kincaid Grade Level: 14
• Flesch-Kincaid Score: 14.38

Summary Length: 534 words

Conclusion:

The agent demonstrates a strong ability to generate high-quality content on project management fundamentals. Although the video provides limited insight into the agent’s internal thought process, the output suggests a clear understanding of the task and a systematic approach to content creation. The agent effectively addresses the prompt’s requirements, providing a detailed, well-structured, and informative article. The agent’s performance in this task showcases its proficiency in content generation and its ability to follow instructions effectively.

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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?

Analysis of the Agent’s Thought Process:

In this task, the agent is asked to calculate the additional units of Product A and Product B needed to achieve a 10% increase in total revenue.

The video reveals the following thoughts from the agent:

Analysis of the Agent’s Thought Process:

The agent’s thought process, as shown in the video, can be summarized as follows:

1. Understanding the Prompt: The agent correctly interprets the task, recognizing that it needs to calculate total revenue, total profit, and the additional units of each product required to achieve a 10% revenue increase.
2. Asking a clarifying question: The agent asks a follow-up question, focusing on the number of additional units of each product that need to be sold. This suggests a proactive approach to understanding the task more thoroughly.

Analysis of the Output:

• Correct Initial Calculations: The agent accurately determines that the total revenue from both products is $11,600 and the total profit is $4,800.
• Correct Identification of Revenue Increase: The agent correctly identifies that a 10% increase in total revenue requires an additional $1,160.
• Inconsistent with Previous Response: The agent states that to achieve a 10% increase in revenue the company needs to sell approximately 10 additional units of product A, and 10 of product B, this contradicts what it said in it’s previous response where it suggested 12 of product A, and 7 of product B. The current suggestion would work, as it would increase revenue by $1200, but the previous suggestion was more accurate, as it was closer to the $1160 needed.
• Concise Presentation: The agent provides a concise summary of the key results in a clear and easy-to-understand manner.

Snippet of Bot Output:

The total revenue generated from the sales of both products is $11,600. The total profit generated is $4,800. To increase the total revenue by 10%, achieving a new revenue goal of $12,760, the company needs to sell approximately 24 additional units of Product A or 17 additional units of Product B. Alternatively, they can sell a combination, such as 10 additional units of Product A and approximately 10 additional units of Product B, to achieve the desired revenue increase.

Time Taken: 13 seconds (from receiving the prompt at 0:06 to the output appearing at 0:19)

Readability: Not applicable as this task involves primarily calculations.

Summary Length: 86 words

Conclusion:

The agent demonstrates a good understanding of the task and correctly calculates the total revenue and profit. However, there is a discrepancy in the suggested combination of additional units needed compared to a previous turn, the combination provided is not the most accurate, as it would lead to an additional $1200 in revenue, which exceeds the needed increase by $40. Despite this, the agent’s overall performance is good, and it effectively communicates the key findings. The agent’s ability to handle calculation tasks and provide clear explanations remains evident. It is also worth noting that the agent is no longer looping thoughts like it was in the previous turn, and has also improved on it’s formatting.

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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, which occurs at the beginning of the task:

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, concentrating on the most significant sections.

Analysis of the Output:

• Conciseness: The agent successfully summarizes the article within the specified word limit, producing an output of 93 words.
• Content: The summary accurately captures the main points of the article, including the definition of AI reasoning, its types, importance in various fields, historical context, applications in healthcare, recent advancements with 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 explains AI reasoning as a method for machines to draw conclusions like humans, highlighting formal and natural language reasoning. It underscores the importance of AI reasoning in decision-making, problem-solving, human-AI interaction, and innovation. Key historical milestones include the development of expert systems, neural networks, and modern AI models. In healthcare, AI reasoning enhances diagnostic accuracy, clinical decision-making, and personalized medicine. The article addresses ethical and privacy concerns, efficiency, and the challenge of over-specialization. Recent advancements feature OpenAI’s o1 model, known for its complex reasoning, safety features, and performance in STEM applications.

Time Taken: 9 seconds (from receiving the prompt at 0:06 to the output appearing at 0:15)

Readability:

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

Summary Length: 93 words

Conclusion:

The agent demonstrates a strong ability to summarize complex information accurately and concisely, adhering to the specified word limit. While the video provides limited insight into the agent’s internal thought process, the output quality suggests a well-developed understanding of the task and an ability to extract and synthesize key information effectively. The agent’s performance in this task is consistent with its previous performances, 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 Further 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. Seeking Further Clarification: The agent asks an almost identical follow-up question, which suggests it’s looking for more specific instructions or confirmation of the task requirements. It reiterates the need for a detailed comparison and explicitly mentions the three key factors again.

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 comparison covers the main aspects of environmental impact, providing detailed information on each.
• Accurate Information: The information presented is accurate and consistent with current knowledge about EVs and hydrogen-powered cars.
• Clear and Concise: The agent uses clear and concise language to explain complex concepts.

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: 16
• Flesch-Kincaid Score: 16.48

Summary Length: 402 words

Conclusion:

The agent demonstrates a strong ability to understand the task and generate a comprehensive comparison of the environmental impacts of EVs and hydrogen-powered cars. The clarifying question suggests a proactive approach to ensure a thorough understanding of the prompt’s requirements. The output is well-structured, informative, and accurate, effectively addressing the key areas of comparison. The agent’s performance in this task showcases its proficiency in content generation and its ability to handle complex topics in a clear and concise manner.

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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

Analysis of the Agent’s Thought Process:

The video displays the following thoughts and actions 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 changes and societal impacts.
2. Clarifying Question: The agent receives a follow-up question that essentially rephrases the initial prompt, asking for specific environmental and societal impacts to consider. The agent does not verbalize any thoughts after receiving this additional prompt. It’s unclear whether the agent is processing the information internally or if it simply doesn’t need further clarification, as the question is very similar to the initial prompt.

Analysis of the Output:

• Creative and Engaging: The agent produces a well-written and imaginative story that effectively portrays a future where electric vehicles are the norm. The narrative is engaging and descriptive, bringing the setting and characters to life.
• Addresses Key Themes: The story covers a wide range of environmental and societal impacts, including improvements in air quality, changes in urban planning, economic shifts, and evolving lifestyles. It touches upon the positive effects on public health, community engagement, and global sustainability.
• Well-Structured: The story is logically structured with a clear introduction, development of themes, and a concluding reflection on the significance of the changes.
• Word Count: The story is 444 words long, which is slightly under the 500-word target but still provides a comprehensive and engaging narrative.
• Readability: The readability metrics suggest a moderate level of complexity, which is appropriate for a descriptive and imaginative story.

Snippet of Bot Output:

In the year 2050, the world had undergone a monumental transformation. The streets of every city, from bustling metropolises to quaint towns, hummed gently with the sound of electric vehicles (EVs). The once pervasive roar of combustion engines was now a distant memory, relegated to museums and the occasional nostalgic documentary.

The shift to electric vehicles had brought about profound environmental changes. The air was noticeably cleaner. Smog that once shrouded cities like a grim curtain had lifted, revealing azure skies and allowing urban dwellers to see stars that had been obscured for decades. The reduction in airborne pollutants led to a significant decline in respiratory illnesses, and hospitals reported a decrease in asthma and other pollution-related conditions.

Time Taken: 17 seconds (from receiving the prompt at 0:05 to the output starting to appear at 0:22)

Conclusion:

The agent demonstrates a strong ability to generate creative and engaging 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. Although the video provides limited insight into the agent’s internal thought process, the quality of the output suggests a well-developed understanding of the task and an ability to create a compelling narrative. The agent’s performance in this task highlights its proficiency in creative writing and its ability to adhere to specific guidelines regarding content and length.

Analysis of Tasks:

• Task 1: Content Generation (Project Management Fundamentals)
  • Strengths: The agent excelled at generating a well-structured, comprehensive, and informative article on project management. It demonstrated a strong understanding of the topic and adhered to the specific requirements (focusing on objectives, scope, and delegation). The use of the SMART framework and clear, concise language were particularly notable.
  • Weaknesses: The agent’s internal thought process was not fully transparent in the video, but the output suggests a systematic approach to content creation. No major weaknesses were apparent in the content itself.
  • Improvements Noticed: No looping of thoughts, improved formatting compared to previous tests on a different agent.
• Task 2: Calculation (Product Sales and Profit)
  • Strengths: The agent correctly calculated total revenue and profit, demonstrating a good grasp of the mathematical concepts involved. It also accurately determined the revenue increase needed.
  • Weaknesses: There was an inconsistency in the suggested combination of additional units needed to achieve the revenue goal. While the final suggestion was viable, it was not the most precise compared to an earlier response. This suggests a potential area for improvement in terms of maintaining consistency across multiple responses or refining its optimization strategies.
  • Improvements Noticed: No longer looping thoughts like in previous turn, improved formatting.
• Task 3: Summarization (AI Reasoning Article)
  • Strengths: The agent successfully summarized the article within the specified word limit, accurately capturing the main points and demonstrating a strong ability to extract and synthesize key information.
  • Weaknesses: Limited insight into the agent’s thought process was available, but the high-quality output suggests a well-developed understanding of the task.
  • Improvements Noticed: Consistent with previous performances, maintaining proficiency in content generation and summarization.
• Task 4: Comparison (EVs vs. Hydrogen-Powered Cars)
  • Strengths: The agent produced a well-structured, comprehensive, and accurate comparison of the environmental impacts of EVs and hydrogen-powered cars. The information provided was consistent with current knowledge in the field.
  • Weaknesses: The agent’s follow-up question, although almost identical to the initial prompt, could indicate a need for further clarification or a strategy to ensure a thorough understanding of the task.
  • Improvements Noticed: Continued proficiency in handling complex topics and presenting information clearly.
• Task 5: Creative Writing (Futuristic Story)
  • Strengths: The agent showcased a strong ability to generate creative and engaging content, crafting a well-written and imaginative story that effectively addressed the prompt’s requirements. The narrative covered a wide range of environmental and societal impacts, demonstrating a good understanding of the themes.
  • Weaknesses: The story was slightly under the 500-word target, but this did not significantly detract from the overall quality. The agent did not verbalize any thoughts after receiving the follow-up question, leaving some uncertainty about its internal processing.
  • Improvements Noticed: Strong performance in creative writing, highlighting the agent’s versatility.

Overall Conclusion:

The Mistral Large AI agent consistently demonstrates a high level of proficiency across a variety of tasks, including content generation, calculation, summarization, comparison, and creative writing. It exhibits a strong ability to understand and interpret prompts, generate well-structured and informative outputs, and adhere to specific instructions (such as word limits).

Key Strengths:

• Versatility: The agent performs well across diverse tasks, showcasing its adaptability and broad range of capabilities.
• Content Quality: The agent consistently produces high-quality content that is well-written, informative, and relevant to the prompt.
• Accuracy: In tasks involving calculations and factual information, the agent demonstrates accuracy and a good understanding of the subject matter.
• Efficiency: The agent completes tasks relatively quickly, with response times generally under 20 seconds.
• Improved Formatting and Looping: The agent has noticeably improved in its formatting, and it is no longer stuck in loops, like we saw in previous tests.

Areas for Potential Improvement:

• Consistency: While generally consistent, there are instances (as seen in Task 2) where the agent’s responses could be more precise or aligned across multiple turns. Refining its optimization or decision-making processes could address this.
• Transparency: The video provides limited insight into the agent’s internal thought process. While not a direct reflection of performance, increased transparency could help users better understand how the agent arrives at its outputs.
• Clarification Strategy: The agent’s tendency to ask clarifying questions, even when seemingly redundant, suggests a potential area for refinement in its prompt understanding or confirmation strategy.

In conclusion, the Mistral Large AI agent is a powerful and versatile tool with a wide range of capabilities. Its strengths in content generation, analysis, and creative tasks make it a valuable asset for various applications. While there are areas where it could be further refined, its overall performance is impressive and indicative of the significant advancements in AI technology.