Exploring AI Agents: How Gemini 1.5 Flash 8B Thinks

Introduction

AI agents have become essential tools in tackling complex challenges, and each new model brings advancements in speed, accuracy, and reasoning. Among these, Gemini 1.5 Flash 8B emerges as a powerful agent capable of processing large-scale information with remarkable precision. But how does it “think,” and what enables its impressive capabilities?

In this blog, we’ll explore the inner workings of Gemini 1.5 Flash 8B, breaking down its architecture, thought processes, and decision-making strategies. By testing its performance in real-world scenarios, we’ll uncover how this model analyzes inputs, applies logic, and delivers actionable insights. Whether you’re an AI enthusiast or seeking practical knowledge about Gemini 1.5 Flash 8B, this article will provide a detailed understanding of how it operates and why it stands out in the world of AI agents.

---

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:

• Understanding the Prompt: The agent correctly interprets the prompt, recognizing that it needs to provide information on project management fundamentals, with a specific focus on objectives, scope, and delegation.
• Information Gathering Strategy: The agent decides to use both Google Search and Wikipedia, which is a reasonable approach to gather both general and specific information on the topic.
• Tool Usage: The agent identifies and uses the google_serper and wikipedia tools, demonstrating an understanding of the available resources and their appropriate use.
• Focus on Key Concepts: The agent’s thoughts explicitly mention the key concepts it needs to address: objectives, scope, and delegation.

Analysis of the Output:

• Well-Structured: The output is well-organized, with clear sections for Defining Project Objectives, Defining Project Scope, Delegation Principles, and a Conclusion.
• Comprehensive: The agent covers the fundamentals of project management as requested, providing definitions, explanations, and examples for each key concept.
• Clear and Concise: The writing is generally clear and concise, although some sentences are a bit long and complex.
• Informative: The output provides valuable insights into project management principles, particularly the importance of SMART objectives, scope management, and effective delegation.
• Actionable: The agent provides practical examples of SMART objectives and key principles of delegation, making the information more actionable for the reader.

Snippet of Bot Output:

Project Management Fundamentals: Defining Objectives, Scope, and Delegation

Project management is a structured approach to planning, organizing, and executing tasks to achieve specific goals within a defined timeframe and budget. A crucial aspect of successful project management lies in clearly defining objectives, understanding the project scope, and effectively delegating tasks.

Defining Project Objectives:

Clear, measurable, achievable, relevant, and time-bound (SMART) objectives are essential for project success. Objectives should be specific, outlining exactly what the project aims to accomplish. Measurable objectives allow for tracking progress and evaluating success. Achievable objectives are realistic and attainable given available resources and constraints. Relevant objectives align with the overall strategic goals of the organization. Time-bound objectives establish deadlines and ensure timely completion.

Time Taken: The agent receives the prompt at 0:09, and the final output appears at 0:23. So the total time taken is 14 seconds.

Readability:

• Flesch-Kincaid Grade Level: 14
• Flesch-Kincaid Score: 14.36
• Summary Length: 346 words

Conclusion:

The agent successfully generates a comprehensive and well-structured overview of project management fundamentals, with a focus on defining objectives, scope, and delegation. The response demonstrates a good understanding of the topic and effectively utilizes the google_serper and wikipedia tools to gather relevant information. The output is informative, actionable, and well-organized, providing valuable insights for anyone involved in project management. Overall, the agent performs well on this task, showcasing its ability to synthesize information and present it in a clear and concise manner.

---

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 and Information Gathering

Analysis of the Agent’s Thought Process:

• Understanding the Prompt: The agent initially understands the need to calculate total revenue and profit, but the thought process shown in the video is repetitive and doesn’t reflect a clear plan of action, it also gets stuck in a loop where it repeats itself for a very long time.
• Lack of Tool Usage: The agent states “Using tool: None” repeatedly, indicating it’s not utilizing any external tools for calculation or information gathering. This is unusual for a task involving numerical calculations, the agent should be using a calculator tool.
• Manual Calculation: The agent mentions manually calculating revenue and profit, which is inefficient and prone to errors.
• New Prompts: The agent receives new prompts during its thought process, suggesting it might be struggling with the initial task or the user is trying to guide it.
• Struggling with Distribution: When asked about distributing additional units, the agent attempts to use the url_crawl_tool which seems inappropriate for this task. It then correctly identifies the need to determine the target revenue increase and calculate required units based on selling prices.

Analysis of the Output:

• Correct Answers: Despite the inefficient thought process, the agent provides the correct answers for total revenue (11,600),totalprofit(11,600),totalprofit(4,800), and the additional units needed for each product (12 for Product A, 9 for Product B).
• Lack of Explanation: The output simply states the answers without explaining the calculations or the reasoning behind the 50/50 split strategy.
• Missed Opportunity: The agent doesn’t discuss alternative distribution strategies or factors that might influence the decision, as prompted by the user.

Snippet of Bot Output:

The total revenue generated from the sales of both products last month was $11,600. The total profit generated from the sales of both products was $4,800. To achieve a 10% increase in total revenue, the company needs to sell an additional 12 units of Product A and 9 units of Product B, assuming a 50/50 revenue split strategy for the additional sales.

Time Taken: The agent receives the prompt at 0:06 and shows the first thought at 0:18, the new prompt at 0:53 and shows the next thought at 0:54, then a new prompt at 1:24, then another prompt at 1:39, then another new prompt at 5:36 and the final output appears at 6:54. So the total time taken is 6 minutes and 48 seconds.

Conclusion:

The agent eventually arrives at the correct answers, but its thought process, as displayed in the video, is highly inefficient and lacks a clear strategy. The agent fails to utilize appropriate tools for calculation and relies on manual methods. Additionally, it struggles to adapt to new prompts and gets stuck in a repetitive loop. The final output, while numerically correct, lacks explanation and fails to fully address the user’s questions about distribution strategies. Overall, the agent’s performance on this task is poor, indicating significant room for improvement in its problem-solving, tool usage, and communication capabilities. The excessive time taken also highlights the agent’s inefficiency.

---

Task 3: Summarization

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

Initial Task Understanding and Information Gathering

Analysis of the Agent’s Thought Process:

• Understanding the Prompt: The agent correctly interprets the prompt, recognizing that it needs to summarize the key findings of the article within a 100-word limit, focusing on the most important sections.
• Information Gathering: The agent appears to have processed the entire article and extracted what it considers the key findings, although the thought process shown is a single block of text, which doesn’t show how it broke down the information. It also seems like the agent is adding extra information that wasn’t included in the prompt.
• Focus on Key Sections: The agent’s output and the thought shown focuses on specific sections like the definition of AI reasoning, its importance, historical development, applications in healthcare, implications, and recent advancements. These are indeed the most significant sections of the article.

Analysis of the Output:

• Structure and Content: The agent’s summary is well-structured, covering the main points of the article in a logical order. It starts with the definition, then moves to importance, history, applications, implications, and recent advancements.
• Conciseness: The summary is concise and stays within the 100-word limit.
• Accuracy: The information presented in the summary accurately reflects the content of the original article.
• Clarity: The summary is clear and easy to understand, effectively conveying the main ideas of the article.
• Key Findings: The agent successfully identifies and summarizes the key findings, including the definition of AI reasoning, its role in enhancing decision-making, the evolution of AI systems, applications in healthcare, ethical considerations, and recent advancements like the o1 model.

Snippet of Bot Output:

Definition of AI Reasoning: AI reasoning is a logical method enabling machines to draw conclusions, predict, and solve problems akin to human thought. It involves using available information to derive new insights or make decisions, mimicking the human brain’s information processing. Two main types exist: formal reasoning (rule-based, mathematical logic) and natural language reasoning (handling human language ambiguity).

Time Taken: The agent receives the prompt at 0:04, and the final output appears at 1:26. So the total time taken is 1 minute and 22 seconds.

Readability:

• Summary Length: 91 words

Conclusion:

The agent effectively summarizes the key findings of the article within the specified word limit. It accurately captures the main points and presents them in a clear and concise manner. The agent demonstrates a good understanding of the article’s content and successfully identifies the most significant sections. Overall, the agent performs well on this summarization task, showcasing its ability to extract and synthesize information effectively. The agent’s performance would have been better if it showed how it went through the thought process and if it did not add extra information into the thought process that wasn’t included in the prompt text.

---

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:

• Understanding the Prompt: The agent correctly interprets the prompt, recognizing that it needs to compare the environmental impacts of electric vehicles (EVs) and hydrogen-powered cars, considering factors like energy production, vehicle lifecycle, and emissions.
• Information Gathering Strategy: The agent decides to use both google_serper and url_crawl_tool. This suggests an intention to conduct a broad search using google_serper and then potentially delve into specific URLs for more detailed information using url_crawl_tool. The specific URLs it tried to crawl are shown in the final output.
• Tool Usage: The agent identifies and uses appropriate tools for its task: google_serper for general search and url_crawl_tool for potentially accessing specific web pages.
• Focus on Key Factors: The prompt explicitly mentions considering energy production, vehicle lifecycle, and emissions. The agent’s output demonstrates that it successfully focused on these factors in its comparison.

Analysis of the Output:

• Well-Structured: The output is well-organized with a clear structure, including sections for Energy Production, Vehicle Lifecycle, Emissions, Other Factors, and a Conclusion. This makes the comparison easy to follow and understand.
• Comprehensive: The agent covers the major environmental considerations for both EVs and HFCVs, including electricity sources, hydrogen production methods, manufacturing impacts, emissions, efficiency, refueling/recharging, cost, and infrastructure.
• Clear and Concise: The writing is generally clear, concise, and easy to understand. It avoids overly technical jargon and explains complex concepts in a straightforward manner.
• Informative: The output provides a detailed comparison, highlighting the dependence on electricity sources for EVs and hydrogen production methods for HFCVs. It also covers other important aspects like efficiency, refueling/recharging, cost, and infrastructure.
• Balanced: The agent presents a balanced comparison, acknowledging the advantages and disadvantages of both technologies.
• Well-Researched: The agent’s output demonstrates that it has gathered information from multiple sources, as indicated by the use of google_serper and url_crawl_tool. The final output also provides links to these sources, adding credibility to the comparison.

Snippet of Bot Output:

Energy Production:

• Electric Vehicles (EVs): EVs run on electricity, which can be generated from various sources. These sources include fossil fuels (coal, natural gas), nuclear power, and renewable energy (solar, wind, hydro). The environmental impact of EVs is heavily dependent on the source of electricity. If the electricity comes from renewable sources, the carbon footprint is significantly lower. However, if the electricity is generated from fossil fuels, the environmental benefits of EVs are reduced.
• Hydrogen Fuel Cell Vehicles (FCVs): FCVs use hydrogen to produce electricity. The environmental impact of FCVs is largely determined by how the hydrogen is produced. “Grey hydrogen” is produced from fossil fuels through a process called steam methane reforming, which releases significant amounts of CO2. “Green hydrogen” is produced through electrolysis, using renewable energy to split water into hydrogen and oxygen, resulting in a much lower carbon footprint. Currently, most hydrogen production relies on fossil fuels, making it less environmentally friendly.

Time Taken:

The agent receives the prompt at 0:06, and the final output appears at 1:52. So the total time taken is 1 minute and 46 seconds.

Readability:

• Flesch-Kincaid Grade Level: 15
• Flesch-Kincaid Score: 14.89
• Summary Length: 698 words

Conclusion:

The agent successfully generates a comprehensive and well-structured comparison of the environmental impacts of EVs and HFCVs. The response demonstrates a good understanding of the topic and effectively utilizes the google_serper and url_crawl_tool to gather relevant information. The output is informative, balanced, and well-researched, providing valuable insights for anyone interested in this topic. The agent’s performance on this task is excellent, showcasing its ability to synthesize information, present it clearly, and support its claims with relevant sources.

---

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 Information Gathering

Analysis of the Agent’s Thought Process:

• Understanding the Prompt: The agent correctly interprets the prompt, recognizing that it needs to generate a creative piece about a future where electric vehicles (EVs) have completely replaced combustion-engine vehicles. It understands the need to address both environmental and societal changes.
• Two-Part Prompt: The interaction is broken into two parts. First, the agent is asked about environmental changes, and then about societal impacts. This suggests the user is guiding the agent to consider these two aspects separately before potentially combining them into a creative narrative.
• Information Synthesis: The agent’s responses indicate that it is synthesizing information from its training data to generate plausible scenarios for a world dominated by EVs. It considers various factors like air quality, noise pollution, infrastructure, industry, and social equity.

Analysis of the Output:

• Creative and Engaging: The agent produces a well-written and engaging narrative titled “Echoes of Tomorrow,” painting a vivid picture of a future transformed by electric vehicles. The title itself is evocative and sets the tone for the piece.
• Well-Structured: The narrative follows a logical progression, starting with the immediate environmental impacts, then moving to societal changes, challenges, and a concluding reflection on the future.
• Comprehensive: The agent covers a wide range of environmental and societal impacts, including air quality, noise pollution, energy consumption, infrastructure development, economic shifts, social equity, and even the impact on wildlife.
• Balanced Perspective: The narrative acknowledges both the positive and negative aspects of the transition, such as the challenges of increased electricity demand, the need for sustainable battery production, and the potential for social inequalities.
• Imaginative and Thought-Provoking: The agent successfully creates a believable and thought-provoking vision of the future, exploring both the utopian possibilities and the potential challenges of a world dominated by EVs.
• Strong Narrative Voice: The narrative has a consistent and engaging voice, using descriptive language and a hopeful tone to draw the reader into the story. The concluding paragraph, with its imagery of a sunset and “echoes of combustion engines,” is particularly effective.

Snippet of Bot Output:

In the year 2050, the world had finally turned a corner. The air was cleaner, skies clearer, and cities quieter. Electric vehicles, once a futuristic dream, had fully replaced their combustion-engine ancestors. Streets once choked with the fumes of gasoline engines now hummed softly with the gentle whir of electric motors, a testament to humanity’s shift towards a greener future.

Time Taken: The agent receives the prompt at 0:04, and the final output appears at 2:25. So the total time taken is 2 minutes and 21 seconds.

Readability:

• Flesch-Kincaid Grade Level: 13
• Flesch-Kincaid Score: 12.59
• Summary Length: 511 words

Conclusion:

The agent excels in this creative writing task, demonstrating a strong ability to generate imaginative and well-structured narratives. The response effectively addresses the prompt, exploring a wide range of environmental and societal impacts in a balanced and thought-provoking manner. The agent’s ability to craft a compelling story with a consistent narrative voice is particularly impressive. The two-part prompting approach seems to have helped the agent organize its thoughts and produce a more comprehensive response. Overall, the agent performs exceptionally well on this task, showcasing its creative potential and its capacity to generate high-quality, engaging content.

Gemini 1.5 Flash 8B: A Promising but Imperfect AI Agent

Gemini 1.5 Flash 8B demonstrates significant potential as an AI agent, excelling in tasks that involve information retrieval, structured summarization, and creative writing. It effectively utilizes tools like google_serper and wikipedia to gather information and generate well-organized, informative, and engaging content. Its performance in the project management, environmental comparison, and creative writing tasks showcases its ability to synthesize information, produce clear and concise outputs, and adapt to different writing styles.

However, the model’s performance in the calculation task reveals critical weaknesses. Its thought process becomes repetitive and inefficient, failing to utilize appropriate calculation tools and relying on manual, error-prone methods. This leads to excessive processing time and a lack of transparency in its reasoning. While it eventually arrives at correct answers, the journey is unnecessarily convoluted, and the output lacks depth in explaining the solution or considering alternative strategies. Additionally in the summarization task, the agent went off-prompt and provided additional information in the thought process that it was not asked for.

Overall Conclusion

Overall, Gemini 1.5 Flash 8B is a powerful tool for tasks requiring language understanding, information retrieval, and creative generation. Its ability to produce well-structured, comprehensive, and engaging content is impressive. However, its current limitations in numerical reasoning and problem-solving, as highlighted by the calculation task, indicate a need for significant improvement in these areas. Future development should focus on enhancing its mathematical capabilities, integrating appropriate tools, and refining its thought process to be more efficient and transparent. This would mean fixing the looping behavior shown in the calculation task. The model would also benefit from stronger adherence to instructions, especially avoiding providing extra information when it’s not requested.

In its current state, Gemini 1.5 Flash 8B is best suited for tasks that leverage its strengths in language and creativity, while caution should be exercised when employing it for tasks requiring complex calculations or intricate problem-solving. As the model continues to evolve, addressing these identified weaknesses will be crucial to unlocking its full potential as a versatile and reliable AI agent.