How AI Agents Like Llama 3.2 1B Process Information

Introduction

AI agents are redefining how we handle information, solve problems, and make decisions. Among these agents, Llama 3.2 1B stands out for its compact yet powerful architecture, designed to process information with efficiency and accuracy. But how well does it perform when tasked with real-world challenges?

In this blog, we’re taking a hands-on approach to test Llama 3.2 1B and evaluate how it reasons as an AI agent. Through practical scenarios, we’ll explore its ability to process inputs, analyze data, and deliver insightful solutions. By examining its performance, we’ll uncover the strengths, weaknesses, and potential of Llama 3.2 1B in dynamic applications. If you’re intrigued by the mechanics of AI reasoning, join us as we put this lightweight yet powerful model to the test.

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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 Planning

Output Snippet:

The output is an article titled “Project Management Fundamentals: Defining Objectives, Scope, and Delegation”. It provides a detailed explanation of these three key aspects of project management.

Time Taken: The agent starts working on the task at 0:06 and the final output is displayed at 0:32, indicating a time taken of 26 seconds.

Readability:

• Flesch-Kincaid Grade Level: 13
• Flesch-Kincaid Score: 12.97

Summary Length: 346 words

Analysis of the Agent’s Thought Process:

The agent’s thought process, as shown in the video, reveals the following:

1. Task Understanding: The agent correctly identifies the task requirements based on the initial prompts. It understands that it needs to provide information on project management fundamentals, particularly focusing on objectives, scope, and delegation.
2. Strategic Tool Selection: The agent initially opts for Wikipedia to get a broad overview, then strategically switches to Google Search (google_serper) for more specific and targeted information.
3. Iterative Research: The agent demonstrates an iterative approach to research, refining its search strategy based on the information it gathers. It first focuses on objectives, then scope, and finally delegation.
4. Adaptability: The agent adapts its approach when the initial Wikipedia search doesn’t provide sufficient detail, showcasing flexibility in problem-solving.

Conclusion:

The agent successfully generates a well-structured and informative article on project management fundamentals. The thought process, although limited to what is shown in the video, suggests a logical and strategic approach to research and content generation. The agent effectively utilizes the available tools and adapts its strategy to gather the necessary information. The output is of high quality, well-organized, and adheres to the prompt’s requirements. The agent continues to demonstrate strong performance in content generation tasks.

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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 only shows a single thought from the agent at the beginning of the task:

1. Understanding the Prompt: The agent correctly interprets the task. It understands that it needs to calculate total revenue, total profit, and the additional units of each product required to achieve a 10% revenue increase.

Bot Output Snippet:

Agent’s final answer: To calculate the total revenue and total profit from sales:

For Product A:

• Total revenue = (120 units * $50) = $6000
• Total production cost = (120 units * $30) = $3600
• Total profit = $6000 – $3600 = $2400

For Product B:

• Total revenue = (80 units * $70) = $5600
• Total production cost = (80 units * $40) = $3200
• Total profit = $5600 – $3200 = $2400

Combined results:

• Total revenue = $6000 + $5600 = $11600
• Total profit = $2400 + $2400 = $4800

To increase total revenue by 10%:

• Target revenue = 11600+(11600+(11600 * 0.10) = $12760

Time Taken: 16 seconds (from receiving the prompt at 0:06 to when the output starts appearing at 0:22)

Readability: Not applicable as the output is primarily calculations.

Summary Length: The final answer is summarized as: “The total revenue generated from the sales of both products is $11,600, and the total profit is $4,800. To achieve a 10% increase in total revenue, the company needs to sell approximately 30 additional units of Product A and 20 additional units of Product B.” (59 words)

Analysis of the Output:

• Accurate Calculations: The agent correctly calculates the total revenue, total profit, and target revenue.
• Clear Presentation: The calculations are presented in a clear and easy-to-understand format.
• Incomplete Solution: The agent does not explicitly show the calculations for determining the additional units needed for each product or the combination. It directly states the answer without showing the steps, which deviates from a fully detailed thought process. This is a change from the previous turn, where it at least displayed an attempt to set up an equation, but made errors in the final answer.
• Incorrect Answer: The suggested combination of additional units (30 of Product A and 20 of Product B) is not entirely accurate. (30*70) = $2900, this exceeds the needed $1160 in additional revenue by $1740.

Conclusion:

The agent demonstrates an understanding of the task and performs the initial calculations correctly. However, the lack of detailed steps for determining the additional units needed and the incorrect final combination provided in the output indicate a potential gap in the agent’s process or a limitation in the information displayed in the video. While the agent arrives at a partially correct answer, the reasoning behind the specific combination of additional units is not transparent.

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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:

only one thought from the agent, which is a direct restatement of the task prompt:

1. Understanding the Prompt: The agent correctly identifies the task as summarizing the key findings of the previous article within a 100-word limit. It also acknowledges the need to focus on the most significant sections.

Analysis of the Output:

• Conciseness: The agent successfully delivers a summary that is within the word limit, coming in at 97 words.
• Content: The summary accurately captures the main topics discussed in the article, including AI reasoning, its types, historical development, applications, recent advancements (OpenAI’s o1 model), and ethical considerations.
• Structure: The summary is well-structured and easy to read, presenting the information in a logical order.
• Clarity: The language used is clear and concise, effectively conveying the key concepts of the article.

Snippet of Bot Output:

The article delves into the realm of AI reasoning, exploring its types, historical evolution, and applications in healthcare and beyond. It underscores the role of AI reasoning in decision-making, problem-solving, and fostering innovation. A specific focus is placed on recent advancements such as OpenAI’s GPT-3 Model, detailing its features and performance. Furthermore, the article raises awareness about the challenges and ethical considerations surrounding AI reasoning models. Overall, it provides a comprehensive overview of the significance and implications of AI reasoning in various fields, shedding light on its potential and limitations.

Time Taken: 10 seconds (from receiving the prompt at 0:06 to providing the final answer at 0:16)

Readability:

• Flesch-Kincaid Grade Level: 19
• Flesch-Kincaid Score: 19.09

Summary Length: 97 words

Conclusion:

The agent demonstrates a strong ability to summarize complex information accurately and concisely. It adheres to the word limit and effectively conveys the key findings of the article. However, the video provides minimal insight into the agent’s internal thought process, making it difficult to analyze its strategies for selecting and condensing information. Despite the limited visibility into the agent’s reasoning, the output quality is high, and the agent successfully completes the task as instructed. The agent continues to perform well and follow the established guidelines for analysis.

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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 Information Gathering

Analysis of the Agent’s Thought Process:

The video reveals the following thoughts from the agent during this task:

1. Understanding the Task: The agent correctly interprets the task, understanding that it needs to compare the environmental impacts of EVs and hydrogen-powered cars, focusing on energy production, vehicle lifecycle, and emissions.
2. Information Gathering: The agent decides to use google_serper for its initial research, aiming to find comprehensive studies or reports. This is a reasonable approach for gathering information on the topic.
3. Using url_crawl_tool: The agent plans to use the url_crawl_tool to fetch content from the links found during the search. It recognizes the need for detailed data on energy production, vehicle lifecycle, and emissions.
4. Error Handling: When an error occurs with the url_crawl_tool, the agent identifies the potential issue as an incorrect input format (not a JSON array of strings) and decides to retry with the correct format. This demonstrates adaptability and problem-solving skills.
5. Repetition: The agent repeats its thoughts about the comparison task and the need to use url_crawl_tool multiple times, and there are long pauses where the agent seems to be doing nothing. This is similar to the last turn, where it kept repeating the same thought. The agent might be experiencing some issues that we are not able to see in this format.

Snippet of Bot Output:

Agent’s final answer:

Environmental Impact Comparison Between Electric Vehicles (EVs) and Hydrogen-Powered Cars

Understanding the environmental impacts of EVs and hydrogen-powered cars requires a comprehensive analysis of energy production, vehicle lifecycle, and emissions. Below is a detailed comparison of these factors for both vehicle types.

1) Energy Production

Electric Vehicles (EVs):

• Energy Source: EVs are powered by electricity, which can be generated from a variety of sources including renewable energy like solar, wind, or hydroelectric power, or from fossil fuels. The environmental impact largely depends on the energy mix of the grid. In regions with high renewable energy usage, EVs have a significantly lower carbon footprint.

Hydrogen-Powered Cars:

• Energy Production: Hydrogen can be produced via:
  • Steam Methane Reforming (SMR): This method uses natural gas, which is not environmentally friendly due to CO2 emissions.
  • Electrolysis: Using renewable energy to split water into hydrogen and oxygen, which is clean but currently less common due to cost and energy efficiency issues.
  • Biomass Gasification: A potentially sustainable method but not yet widely implemented.

Time Taken: 1 minute and 10 seconds (from receiving the prompt at 0:06 to the final answer appearing at 1:16)

Readability:

• Flesch-Kincaid Grade Level: 17
• Flesch-Kincaid Score: 17.08

Summary Length: 855 words

Analysis of the Output:

• Comprehensive Comparison: The agent provides a detailed comparison of EVs and hydrogen-powered cars, covering energy production, vehicle lifecycle, and emissions.
• Well-Structured: The response is well-organized with clear headings and subheadings, making it easy to follow.
• Accurate Information: The information provided is accurate and reflects the current understanding of the environmental impacts of both technologies.
• Minor Formatting Issues: There are some minor formatting inconsistencies in the output.

Conclusion:

The agent successfully completes the comparison task, delivering a detailed and informative analysis. However, the video only offers limited insight into the agent’s internal thought process, primarily showing its initial understanding of the task, its plan to use specific tools, and its response to an error. The agent’s ability to adapt to the error and continue with the task is positive. Overall, the agent demonstrates proficiency in handling complex comparison tasks and generating high-quality content. The agent is consistently performing well and adhering to the established guidelines for analysis. The agent seems to be looping again however, as it keeps repeating it’s thoughts, this might be causing the agent to provide an incomplete output.

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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 only shows one thought from the agent at the beginning of the task:

1. Understanding the Prompt: The agent correctly identifies the key requirements of the task:
   • Write a futuristic story.
   • Set in a world where electric vehicles have completely replaced combustion-engine vehicles.
   • Include environmental changes.
   • Include societal impacts.
   • Target length of 500 words.

Analysis of the Output:

• Creative and Descriptive: The agent successfully creates a vivid and imaginative story, painting a detailed picture of a future transformed by electric vehicles. The narrative is engaging and uses descriptive language effectively.
• Covers Key Aspects: The story addresses both environmental and societal changes, as requested in the prompt. It touches upon aspects like air quality, urban planning, economic shifts, and lifestyle changes.
• Well-Structured: The narrative has a clear structure, starting with an introduction to the setting and progressing through different aspects of the transformed world.
• Word Count: The agent’s response is 475 words, which is slightly under the requested 500 words, but it still manages to tell a complete and effective story within those parameters.
• 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 as electric vehicles had become the primary mode of transportation in every corner of the globe. The once polluted skies now shimmered with clarity, and the streets hummed with the quiet whirring of electric motors. As the last gas-guzzling cars faded into memory, a new era of sustainable mobility and innovation dawned.

The environmental changes were profound. Gone were the days of smog-choked cities and oil spills despoiling pristine landscapes. The air was fresher, the water cleaner, and the natural world began to heal from decades of human-induced damage. With reduced emissions and a shift towards renewable energy sources to power these vehicles, the planet was on a path towards rejuvenation.

Time Taken: 10 seconds (from receiving the prompt at 0:06 to the output starting to appear at 0:16)

Conclusion:

Despite the limited insight into the agent’s internal thought process provided by the video, the quality of the generated story suggests a strong capability in creative writing. The agent understands the prompt’s requirements and delivers a well-structured, detailed, and engaging narrative within the specified constraints. The agent continues to perform very well in these tasks, demonstrating a high level of proficiency in content generation.

Final Thoughts

Our exploration of Llama 3.2 1B’s performance across diverse tasks provides a compelling snapshot of its capabilities as a compact yet potent AI agent. Despite the constraints of the video format, which offered limited visibility into the agent’s internal thought processes, the quality of its outputs consistently demonstrated its effectiveness and potential.

From generating a comprehensive article on project management fundamentals to accurately tackling complex calculations (though with a hiccup in explaining the final steps of the revenue increase problem), Llama 3.2 1B showcased its versatility. The summarization task highlighted its ability to distill key information concisely, while the comparison task, despite encountering a technical error, ultimately delivered a thorough analysis of the environmental impacts of different vehicle types. The agent’s foray into creative writing further cemented its proficiency, crafting an engaging narrative within the specified parameters.

However, the recurring theme of limited insight into the agent’s “thoughts” was also present in this series of tests. Similar to the previous agent analysis, we were often left with only a single thought per task, primarily reflecting the initial understanding of the prompt. This, coupled with repetitive thoughts, particularly during the comparison task, suggests potential areas for improvement in either the agent’s internal processes or the way its reasoning is visualized. It is important to note that the agent experienced issues with its tools during the comparison task and that this is likely related to the agent’s inability to provide a full response in this turn.

Nevertheless, Llama 3.2 1B’s performance remains impressive. Its ability to generate high-quality content, perform calculations, summarize information, and engage in creative writing highlights its potential as a powerful tool across various applications. This compact model demonstrates that significant capabilities can be packed into smaller AI architectures, opening up possibilities for more efficient and accessible AI solutions. While a deeper understanding of its internal workings would undoubtedly enhance our appreciation of its abilities, this hands-on evaluation confirms that Llama 3.2 1B is a force to be reckoned with in the evolving landscape of AI agents. As this technology continues to advance, it will be fascinating to see how these smaller models are further optimized and deployed in real-world scenarios.