RAG_with_Weaviate_DB.ipynb

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            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client->google-generativeai<0.9.0,>=0.8.0->langchain_google_genai) (4.1.1)\n",
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            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.1->google-ai-generativelanguage==0.6.10->google-generativeai<0.9.0,>=0.8.0->langchain_google_genai) (1.62.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /usr/local/lib/python3.10/dist-packages (from httplib2<1.dev0,>=0.19.0->google-api-python-client->google-generativeai<0.9.0,>=0.8.0->langchain_google_genai) (3.2.0)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=2.15.0->google-generativeai<0.9.0,>=0.8.0->langchain_google_genai) (0.6.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install weaviate-client langchain langchain_google_genai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "WEAVIATE_API_KEY = \"9FdtrS8mShbJ9RgtVbvL0gKUMF0CiZtVA4tE\"\n",
        "WEAVIATE_CLUSTER = \"https://ql4sueietoqf9kwgip5jya.c0.asia-southeast1.gcp.weaviate.cloud\""
      ],
      "metadata": {
        "id": "V-sQraYtBvBv"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "GOOGLE_API_KEY = userdata.get(\"GEMINI_API_KEY\")"
      ],
      "metadata": {
        "id": "ImgACnAWGGGu"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "\n",
        "llm: ChatGoogleGenerativeAI = ChatGoogleGenerativeAI(model = \"gemini-1.5-flash\", api_key = GOOGLE_API_KEY)\n",
        "\n",
        "a = llm.invoke(\"Hi\")\n",
        "print(a.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4w3oGYbeGRpn",
        "outputId": "11fe47a8-53ab-409c-d6ef-eff114ec1cc9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi there! How can I help you today?\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Reading"
      ],
      "metadata": {
        "id": "uuytDEGdCyCl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir data"
      ],
      "metadata": {
        "id": "UBGcj0bxCwgO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfbe21c2-9bbf-4300-b381-18d592addd7d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘data’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install unstructured\n",
        "!pip install \"unstructured[pdf]\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vzf37wCDEHom",
        "outputId": "b006222f-1dee-45fb-fb2b-1e7c0404d08c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U langchain-community"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXnX3cUQIhvO",
        "outputId": "8d61dbb9-24bc-463d-b1e8-65d42880e503"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain<0.4.0,>=0.3.13->langchain-community) (2.23.4)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community) (1.0.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain-community) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain-community) (1.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import DirectoryLoader\n",
        "\n",
        "loader = DirectoryLoader(\"./data\",glob = \"**/*.pdf\")\n",
        "data = loader.load()"
      ],
      "metadata": {
        "id": "noStRcDmCweN"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pdf used: Agentic AI\n",
        "https://www.artificiality.world/content/files/2024/05/Agentic-AI.pdf"
      ],
      "metadata": {
        "id": "DGk0ZxpfKDnG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrWdn1M0EdxX",
        "outputId": "1953734e-52db-4380-b49e-e0db795d2f47"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Agentic AI (aka AI Agents)\\n\\nThe future of AI is agentic.\\n\\nPublished: May 2024\\n\\nKey Points:\\n\\nAgentic AI: Agentic AI systems represent a signiﬁcant advancement in AI, characterized by their ability to\\n\\nperceive, reason, and act autonomously, extending human capabilities in unprecedented ways.\\n\\nDeﬁnitions and Perspectives: Various deﬁnitions from organizations like OpenAI, IBM, and Google\\n\\nDeepMind highlight the multifaceted nature of agentic AI, emphasizing goals, actions, and natural language\\n\\ninterfaces. At Artiﬁciality, we deﬁne agentic AI systems as those that can perceive, reason, and act with varying\\n\\ncomplexity to extend the human mind beyond our current experience.\\n\\nHistorical Context and Development: The concept of AI agency has evolved from early systems like IBM\\'s\\n\\nDeep Blue to advanced platforms such as AutoGPT and LangChain, showcasing rapid advancements in the\\n\\nﬁeld.\\n\\nCore Components: Agentic AI systems are built on three main components:\\n\\nPerception: Utilizing advanced sensors and machine learning for environmental understanding.\\n\\nReasoning: Combining logical reasoning, probabilistic inference, and heuristic decision-making.\\n\\nAction: Executing tasks through pre-deﬁned behaviors, learned skills, and adaptive control.\\n\\nAgentic AI Personas: Different combinations of perception, reasoning, and action create varied AI personas,\\n\\nfrom simple aides to complex wayﬁnders, each suited for speciﬁc applications and levels of complexity.\\n\\nImpacts and Considerations: The deployment of agentic AI has the potential to signiﬁcantly impact daily\\n\\ntasks and strategic decision-making, but also raises concerns about control, ethical considerations, and\\n\\nunintended consequences.\\n\\nDesigning for AI Agents: The shift from customer experience (CX) to agent experience (AX) requires new\\n\\ndesign principles to accommodate AI agents, focusing on efficiency, transparency, and the ability to process vast\\n\\namounts of information.\\n\\nOrganizational Structures: Single-agent and multi-agent architectures (vertical and horizontal) each have\\n\\ndistinct advantages and challenges, inﬂuencing scalability, robustness, and ﬂexibility.\\n\\nArtiﬁciality\\n\\nPage 1\\n\\nhello@artiﬁciality.world\\n\\nIntroduction\\n\\nRecent developments in AI have given rise to a new class of systems known as agentic AI. These systems are\\n\\ncharacterized by their ability to perceive, reason, and act with varying levels of complexity, extending human\\n\\ncapabilities in unprecedented ways. While there isn’t a single deﬁnition of agentic AI—what is agreed upon is that\\n\\nagentic AI represents a signiﬁcant leap.\\n\\nOpenAI describes agentic AI systems as those that\\n\\n\"can pursue complex goals with limited direct\\n\\nsupervision.\" Researchers from Stanford, Microsoft,\\n\\nand UCLA characterize AI agents as \"a class of\\n\\ninteractive systems that can perceive visual stimuli,\\n\\nlanguage inputs, and other environmentally-\\n\\ngrounded data, and can produce meaningful actions.\"\\n\\nIBM views AI agents as \"language model-powered\\n\\nentities able to plan and take actions to execute goals\\n\\nover multiple iterations,\" while Google DeepMind\\n\\ndeﬁnes advanced AI assistants as \"artiﬁcial agents\\n\\nwith natural language interfaces, whose function is to\\n\\nplan and execute sequences of actions on behalf of a\\n\\nuser—across one or more domains—in line with the\\n\\nuser\\'s expectations.\"\\n\\nAt Artiﬁciality, we deﬁne agentic AI systems as those\\n\\nthat can perceive, reason, and act with varying\\n\\ncomplexity to extend the human mind beyond our\\n\\ncurrent experience. This deﬁnition emphasizes the\\n\\ntrio of capabilities—perception, reasoning, and action\\n\\n—that we will use to frame various capabilities of AI\\n\\nagents later in this report.\\n\\nThe concept of AI agency AI is not entirely new, with\\n\\nroots tracing back to early systems like IBM\\'s Deep\\n\\nBlue chess-playing system in 1997 and the debut of\\n\\nvirtual assistants like Siri in 2011. However, recent\\n\\nyears have seen a rapid acceleration in the\\n\\ndevelopment and deployment of increasingly\\n\\nsophisticated agentic AI systems. Early experiments\\n\\nlike AutoGPT and BabyAGI caught the AI\\n\\ncommunity’s attention while newer tools and\\n\\nplatforms like Brevian, CrewAI, LangChain, and\\n\\nQurrent provide the ability for individuals and\\n\\norganizations to develop their own AI agents.\\n\\nArtiﬁciality\\n\\nPage 2\\n\\nhello@artiﬁciality.world\\n\\nWe are focused on agentic AI because we believe a) it has the potential to have wide-ranging impacts on how we\\n\\nlive and work, b) these technologies have the potential to both empower and disempower, and c) some level of\\n\\nagency is the future of AI. Giving machines the agency to perceive, reason, and act to accomplish goals might be\\n\\nquite useful with everyday tasks. But there is also the possibility for AI agents to wreak havoc if left unchecked.\\n\\nSince everyone with a computer now has the ability to create agentic AI, there may be little to no ability to control\\n\\nhow and why these agents do what they do.\\n\\nIn this report, we will explore the key components of agentic AI systems, including perception, reasoning, and\\n\\naction. We\\'ll examine how these capabilities can be combined in various ways to create AI agents with different\\n\\nroles and personas, from simple task executors to complex decision support systems. We\\'ll also consider the\\n\\nrealities of deploying agentic AI in real-world contexts, including the shift from designing for human customers\\n\\nto designing for AI agents themselves. Finally, we\\'ll explore the challenges and opportunities of building multi-\\n\\nagent systems and the importance of assembling diverse, multidisciplinary teams to effectively develop and\\n\\nintegrate agentic AI technologies.\\n\\nIf you thought that LLMs changed everything (which they did), agentic AI will change everything even more, even\\n\\nif its full instantiation is many years away.\\n\\nArtiﬁciality\\n\\nPage 3\\n\\nhello@artiﬁciality.world\\n\\nThe Language of Agentic AI: A Caveat\\n\\n“Can machines think? This should begin with deﬁnitions of the meaning of the terms ‘machine’ and “‘think.’”\\n\\n—Alan Turing\\n\\nThe rapid development of agentic AI systems has sparked a need for new metaphors and mental models to\\n\\ncomprehend their complexity. While these AI agents display intricate behaviors and interactions that surpass\\n\\nsimplistic notions of machines, they remain fundamentally distinct from biological life.\\n\\nWhen we describe advanced AI using terms traditionally reserved for living systems, we must recognize that this\\n\\nlinguistic mapping does not imply that AI possesses life or subjective experiences like that of humans. Instead,\\n\\nthis language of life, cognition, and behavior serves as a tool to help conceptualize the increasingly sophisticated\\n\\ncapabilities of AI, which emerge from complex informational dynamics and goal-directed, optimized functions.\\n\\nAs AI agents become more adaptive, interactive, and autonomous, we recognize they occupy a novel space on a\\n\\nmultidimensional continuum of possible complex systems, characterized by varying degrees of design versus\\n\\nemergence and mechanism versus cognition. Across this continuum, agentic AI may functionally mimic\\n\\nproperties of life—such as perception, reasoning, and action—through rational architectures that enable them to\\n\\npursue goals while interacting with complex environments.\\n\\nHowever, we must emphasize that this functional resemblance to living systems does not equate to AI possessing\\n\\nexistential choice, intentionality, phenomenological experience, or metacognition—qualities that characterize\\n\\nbiological cognition and remain qualitatively distinct from the computational processes of artiﬁcial agents.\\n\\nAs we attempt to describe these new AI capabilities, we face the challenge of developing more precise deﬁnitions\\n\\nand conceptual frameworks to reason about the novel design space of agentic systems. In this report, we lay the\\n\\ngroundwork for this task, aiming to maintain a balanced perspective that includes the use of appropriate\\n\\nmetaphors. By doing so, we hope to provide intellectual clarity as we shape a future in which artiﬁcial agents and\\n\\nliving cognitive systems interact in increasingly ﬂuid and synergistic ways.\\n\\nA key challenge is to develop more precise deﬁnitions and conceptual frameworks to reason about this novel\\n\\ndesign space of possible agentic systems, something we attempt to make a start on in this report. Our goal is to\\n\\nmaintain a balanced perspective—including use of appropriate metaphors—which will provide intellectual\\n\\nclarity as we shape a future in which artiﬁcial agents and living cognitive systems interact in increasingly ﬂuid\\n\\nand synergistic ways.\\n\\nArtiﬁciality\\n\\nPage 4\\n\\nhello@artiﬁciality.world\\n\\nUnderstanding Agentic AI\\n\\nAt the core of agentic AI systems are three key components: perception, reasoning, and action. Each of these\\n\\ncapabilities can be present in an AI agent to varying degrees of complexity, allowing for the creation of systems\\n\\nwith diverse skill sets and areas of specialization. By understanding how these components function and interact,\\n\\nwe can better grasp the potential and limitations of agentic AI.\\n\\nPerception\\n\\nUtilizes advanced sensors and and machine learning models to interpret and understand the environment.\\n\\nIncorporates spatial and visual reasoning capabilities to effectively navigate and interact with the physical\\n\\nworld.\\n\\nCombines intuitive pattern recognition with deliberate analysis to process and make sense of complex\\n\\nsensory information.\\n\\nAdapts and reﬁnes perceptual models based on experience and feedback, enabling the agent to handle novel\\n\\nand changing environments.\\n\\nPerception refers to an AI agent\\'s ability to interpret and make sense of its environment. This involves taking in\\n\\ndata from various sensors or input channels and processing it to extract meaningful information. At a basic level,\\n\\nperceptual capabilities might include simple pattern recognition and feature extraction from structured data.\\n\\nHowever, as the complexity of an agent\\'s perception increases, it can begin to handle more diverse, multi-modal\\n\\nsensory inputs and unstructured data.\\n\\nAdvanced perceptual capabilities in AI agents may include sophisticated spatial and visual reasoning, allowing\\n\\nthem to navigate and interact with the physical world in more natural and intuitive ways. These systems can also\\n\\ndemonstrate continuous adaptation and reﬁnement of their perceptual models, learning from experience to\\n\\nbetter handle novel situations and environments.\\n\\nAs the complexity of an AI agent\\'s perception grows, it can give rise to emergent properties and behaviors. The\\n\\nsystem may begin to exhibit a more holistic and contextual understanding of its environment, integrating\\n\\nmultiple sources of information to form a coherent picture of the world around it.\\n\\nReasoning\\n\\nEmploys a combination of logical reasoning, probabilistic inference, and heuristic decision-making\\n\\nto solve problems and make decisions.\\n\\nBalances fast, intuitive processing (System 1) with slower, more deliberate reasoning (System 2)\\n\\nto efficiently handle a range of cognitive tasks.\\n\\nUtilizes spatial and visual representations to support planning, problem-solving, and creative thinking.\\n\\nArtiﬁciality\\n\\nPage 5\\n\\nhello@artiﬁciality.world\\n\\nIncorporates emergent properties arising from the interaction of various reasoning components, enabling the\\n\\nagent to exhibit intelligent behavior beyond the sum of its parts.\\n\\nConsiders the broader context and complex systems in which the agent operates, accounting for the\\n\\ninterdependencies and feedback loops that shape its reasoning processes.\\n\\nReasoning encompasses an AI agent\\'s ability to process information, draw inferences, and make decisions based\\n\\non the data it has gathered through perception. At a basic level, this might involve predominantly heuristic-based\\n\\nreasoning with a limited ability to handle complex or ambiguous problems. Such systems may rely on narrow,\\n\\ndomain-speciﬁc planning and problem-solving strategies with minimal integration of spatial and visual\\n\\nreasoning.\\n\\nAs the complexity of an agent\\'s reasoning increases, however, it can begin to demonstrate a more balanced\\n\\nintegration of intuitive and deliberate reasoning processes. These advanced systems can tackle ill-deﬁned, open-\\n\\nended problems by employing ﬂexible, adaptive problem-solving strategies that span multiple domains. They\\n\\nmay also incorporate seamless integration of spatial and visual reasoning, allowing them to manipulate and draw\\n\\ninsights from perceptual data in more sophisticated ways for more advanced planning and problem-solving..\\n\\nAt the highest levels of complexity, an AI agent\\'s reasoning may begin to consider the broader context and\\n\\ncomplex system dynamics in which it operates. This could involve explicit modeling of the interconnections and\\n\\nfeedback loops that shape the environment, as well as the ability to anticipate and adapt to emergent phenomena.\\n\\nThe result is the emergence of genuinely intelligent decision-making capabilities that can rival or even exceed\\n\\nhuman reasoning in certain domains.\\n\\nAction\\n\\nExecutes actions and interacts with the environment through a combination of pre-deﬁned behaviors,\\n\\nlearned skills, and adaptive control.\\n\\nEmploys spatial and visual reasoning to guide physical actions and manipulations in the world.\\n\\nBalances intuitive, reactive behaviors with deliberate, goal-directed actions to effectively\\n\\nrespond to different situations.\\n\\nLearns and adapts action strategies based on experience and feedback, allowing the agent\\n\\nto improve its performance over time.\\n\\nConsiders the potential emergent consequences of its actions on the broader system\\n\\nand environment in which it operates.\\n\\nAction refers to an AI agent\\'s ability to interact with its environment and effect change based on its perceptual\\n\\ninputs and reasoning processes. At a basic level, this might involve executing simple, pre-deﬁned action routines\\n\\nor motor skills with limited adaptability or learning from experience.\\n\\nAs the complexity of an agent\\'s action capabilities increases, however, it can begin to demonstrate a more diverse\\n\\nand adaptive action repertoire. This might include sophisticated motor skills and behaviors that allow the agent\\n\\nto manipulate objects, navigate environments, and interact with humans in more natural and intuitive ways.\\n\\nArtiﬁciality\\n\\nPage 6\\n\\nhello@artiﬁciality.world\\n\\nAdvanced action capabilities may also involve deliberate, goal-directed control of actions, with the ability to plan\\n\\nand execute complex sequences of behaviors to achieve desired outcomes.\\n\\nCritically, the development of advanced action capabilities in AI agents often involves the integration of spatial\\n\\nand visual reasoning. By leveraging perceptual data to guide physical interactions, these systems can\\n\\ndemonstrate a more embodied and situated form of intelligence that is grounded in the real world.\\n\\nArtiﬁciality\\n\\nPage 7\\n\\nhello@artiﬁciality.world\\n\\nPersonas\\n\\nThe three components of perception, reasoning, and action can be combined in various ways to create AI agents\\n\\nwith different capabilities and specializations. We can think of these combinations as deﬁning different agent\\n\\npersonas, each with its own strengths, limitations, and potential applications.\\n\\nFor example, an AI agent with low complexity in all three components might function as a simple aide or\\n\\nassistant, carrying out basic tasks with minimal autonomy or adaptability. Such a system might be well-suited to\\n\\nhandling routine data entry, scheduling, or customer service inquiries but would likely struggle with more open-\\n\\nended or ambiguous problems.\\n\\nIn contrast, an agent with high complexity reasoning and action capabilities but low complexity perception\\n\\nmight function as a navigator or decision support system. Such an agent could analyze complex datasets,\\n\\ngenerate novel insights, and propose solutions to strategic problems but would rely on human input for situational awareness and contextual understanding.\\n\\nAt the highest end of the complexity spectrum, we might imagine an AI agent with advanced capabilities across\\n\\nall three components—a \"Wayﬁnder\" persona that can navigate uncharted territories, devise innovative solutions\\n\\nto complex problems, and adapt to changing circumstances with a high degree of autonomy. Such a system\\n\\nwould likely excel in domains requiring a combination of perceptual acuity, creative problem-solving, and\\n\\nsituated action, such as leading complex projects or guiding strategic decision-making.\\n\\nBy understanding the interplay of perception, reasoning, and action in agentic AI systems, we can begin to map\\n\\nout the landscape of possible agent personas and skill sets. This, in turn, allows us to more effectively match AI\\n\\ncapabilities to real-world problems and applications, ensuring that we are deploying these powerful tools in ways\\n\\nthat maximize their potential while minimizing risks and unintended consequences.\\n\\nAgentic AI Personas\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nAide\\n\\nLow\\n\\nLow\\n\\nLow\\n\\nTrailblazer\\n\\nLow\\n\\nLow\\n\\nHigh\\n\\nNavigator\\n\\nLow\\n\\nHigh\\n\\nLow\\n\\nPathﬁnder\\n\\nLow\\n\\nHigh\\n\\nHigh\\n\\nLookout\\n\\nHigh\\n\\nLow\\n\\nLow\\n\\nVoyager\\n\\nHigh\\n\\nLow\\n\\nHigh\\n\\nOracle\\n\\nHigh\\n\\nHigh\\n\\nLow\\n\\nWayﬁnder\\n\\nHigh\\n\\nHigh\\n\\nHigh\\n\\nArtiﬁciality\\n\\nPage 8\\n\\nhello@artiﬁciality.world\\n\\nAide\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nAide\\n\\nLow\\n\\nLow\\n\\nLow\\n\\nDescription: Your dependable companion who carries your everyday burdens, guiding you through the basics\\n\\nwith patience and care.\\n\\nHuman Role: The human acts as a direct supervisor, providing step-by-step instructions and monitoring the\\n\\nAI\\'s performance closely. The human is responsible for breaking down tasks into simple, executable steps and\\n\\nensuring the AI follows them accurately.\\n\\nAI Agent Role: The AI agent acts as a simple executor, following the human\\'s explicit instructions without\\n\\nmuch autonomy or adaptation. The AI\\'s role is to perform speciﬁc, well-deﬁned tasks as directed by the human.\\n\\nUse for:\\n\\nBasic data entry, simple customer service queries, routine scheduling tasks.\\n\\nManaging personal schedules, creating simple shopping lists, helping with basic travel arrangements.\\n\\nDo Not Use for:\\n\\nComplex data analysis, creative problem-solving, strategic decision-making.\\n\\nProviding in-depth life advice, handling complex personal ﬁnances, making important life decisions.\\n\\nTrailblazer\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nTrailblazer\\n\\nLow\\n\\nLow\\n\\nHigh\\n\\nDescription: Your intrepid explorer who forges ahead, efficiently tackling repetitive tasks and clearing the\\n\\npath for your success.\\n\\nHuman Role: The human acts as a task manager, deﬁning speciﬁc goals and constraints for the AI to follow.\\n\\nThe human is responsible for setting up the AI\\'s high-level objectives and monitoring its progress, but does not\\n\\nneed to provide as much direct instruction for the actual execution of tasks.\\n\\nAI Agent Role: The AI agent acts as a skilled operator, autonomously executing complex tasks and adapting to\\n\\nminor variations in the environment. The AI\\'s role is to use its advanced action capabilities to achieve the\\n\\nspeciﬁc goals set by the human, while still relying on human guidance for overall strategy and decision-making.\\n\\nUse for:\\n\\nAutomated manufacturing processes, repetitive data cleanup, high-volume transaction processing.\\n\\nArtiﬁciality\\n\\nPage 9\\n\\nhello@artiﬁciality.world\\n\\nAutomating repetitive household chores, managing routine bill payments, helping with basic tasks.\\n\\nDo Not Use for:\\n\\nHandling nuanced customer interactions, making judgment calls, providing strategic insights.\\n\\nProviding emotional support, offering nuanced advice on personal relationships, handling complex or\\n\\nsensitive family matters.\\n\\nNavigator\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nNavigator\\n\\nLow\\n\\nHigh\\n\\nLow\\n\\nDescription: Your skilled guide through complex information landscapes, who plots the best course through\\n\\ndata and ideas, steering you towards valuable insights and informed decisions.\\n\\nHuman Role: The human acts as a strategic partner, working with the AI to deﬁne high-level goals and\\n\\nstrategies. The human is responsible for providing domain expertise and ethical guidance, while relying on the\\n\\nAI to generate and evaluate potential solutions.\\n\\nAI Agent Role: The AI agent acts as a decision support system, using its advanced reasoning capabilities to\\n\\nanalyze data, generate insights, and propose solutions to the human. The AI\\'s role is to augment human\\n\\ndecision-making by providing informed recommendations, while still relying on the human for ﬁnal approval\\n\\nand execution.\\n\\nUse for:\\n\\nAnalyzing complex datasets, providing data-driven insights, guiding decision-making processes.\\n\\nAnalyzing personal health data, providing insights on ﬁnancial planning, helping to research and compare\\n\\ncomplex products or services.\\n\\nDo Not Use for:\\n\\nPhysically executing tasks, directly interacting with customers, handling sensitive or emotional situations.\\n\\nDirectly intervening in personal situations, providing hands-on assistance with tasks, offering emotional\\n\\nguidance or support.\\n\\nArtiﬁciality\\n\\nPage 10\\n\\nhello@artiﬁciality.world\\n\\nPathfinder\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nPathﬁnder\\n\\nLow\\n\\nHigh\\n\\nHigh\\n\\nDescription: Your resourceful planner and problem-solver, who blazes trails through uncharted territories,\\n\\ndevising innovative strategies and solutions to help you reach your goals.\\n\\nHuman Role: The human acts as a high-level supervisor, setting overall objectives and constraints for the AI to\\n\\noperate within. The human is responsible for monitoring the AI\\'s performance and ensuring its actions align\\n\\nwith human values and priorities, but does not need to be involved in the day-to-day decision-making and\\n\\nexecution of tasks.\\n\\nAI Agent Role: The AI agent acts as an autonomous problem-solver, using its advanced reasoning and action\\n\\ncapabilities to analyze complex situations, devise solutions, and execute them independently. The AI\\'s role is to\\n\\ntake on signiﬁcant responsibility for achieving the objectives set by the human, while still remaining\\n\\naccountable to human oversight.\\n\\nUse for:\\n\\nOptimizing supply chain logistics, developing comprehensive project plans, creating innovative solutions to\\n\\ncomplex problems.\\n\\nCreating detailed plans for personal projects, developing innovative solutions to complex personal challenges,\\n\\noptimizing personal productivity and goal achievement.\\n\\nDo Not Use for:\\n\\nGathering and interpreting raw data, handling physical tasks, providing emotional support.\\n\\nProviding direct physical assistance, handling intimate or sensitive personal matters, offering emotional\\n\\ncomfort or support.\\n\\nLookout\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nLookout\\n\\nHigh\\n\\nLow\\n\\nLow\\n\\nDescription: Your vigilant guardian who keeps a watchful eye on your environment, alerting you to potential\\n\\nrisks and anomalies and helping you stay on course.\\n\\nHuman Role: The human acts as an interpreter, translating the AI\\'s advanced perceptual insights into\\n\\nactionable information. The human is responsible for making sense of the data and patterns identiﬁed by the AI\\n\\nand determining how to use this information to guide decision-making and action.\\n\\nArtiﬁciality\\n\\nPage 11\\n\\nhello@artiﬁciality.world\\n\\nAI Agent Role: The AI agent acts as a sensory processor, using its advanced perceptual capabilities to gather\\n\\nand analyze complex data from the environment. The AI\\'s role is to provide the human with rich, detailed\\n\\ninsights about the world, but relies on the human to interpret this information and determine appropriate\\n\\nactions.\\n\\nUse for:\\n\\nMonitoring security cameras, detecting anomalies in large datasets, identifying potential quality control\\n\\nissues on production lines.\\n\\nMonitoring home security systems, detecting potential issues with personal devices or appliances,\\n\\nidentifying unusual patterns in personal health or ﬁnancial data.\\n\\nDo Not Use for:\\n\\nMaking complex decisions, physically responding to detected issues, providing strategic guidance.\\n\\nMaking judgment calls on personal matters, directly intervening in emergency situations, providing guidance\\n\\non sensitive personal issues.\\n\\nVoyager\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nVoyager\\n\\nHigh\\n\\nLow\\n\\nHigh\\n\\nDescription: Your fearless adventurer who boldly navigates complex, dynamic environments, adapting to new\\n\\nchallenges and discovering opportunities at every turn.\\n\\nHuman Role: The human acts as a supervisor and ethical governor, monitoring the AI\\'s actions and ensuring\\n\\nthey align with human values and priorities. The human is responsible for setting overall objectives and\\n\\nconstraints, but relies on the AI to autonomously execute tasks and adapt to the environment.\\n\\nAI Agent Role: The AI agent acts as an adaptive automaton, using its advanced perception and action\\n\\ncapabilities to interact with the world in sophisticated ways. The AI\\'s role is to autonomously execute tasks and\\n\\nrespond to changes in the environment, while still relying on the human for high-level guidance and ethical\\n\\noversight.\\n\\nUse for:\\n\\nAutonomous exploration of hazardous environments, dynamically adapting to changing conditions in\\n\\nmanufacturing processes, navigating complex virtual environments.\\n\\nExploring virtual worlds for entertainment, adapting to user preferences in personal digital assistants,\\n\\nnavigating complex online resources for personal research.\\n\\nDo Not Use for:\\n\\nProviding strategic direction, making ethical judgments, handling sensitive interpersonal situations.\\n\\nArtiﬁciality\\n\\nPage 12\\n\\nhello@artiﬁciality.world\\n\\nProviding guidance on major life decisions, handling sensitive personal information, offering emotional or\\n\\npsychological support.\\n\\nOracle\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nOracle\\n\\nHigh\\n\\nHigh\\n\\nLow\\n\\nDescription: Your wise and far-seeing guide, who reads the signs and interprets the mysteries, offering\\n\\nprophetic insights and illuminating the path ahead.\\n\\nHuman Role: The human acts as a collaborator and co-creator, working with the AI to generate new ideas and\\n\\nsolutions. The human is responsible for providing creative insight and domain expertise, while relying on the AI\\n\\nto process complex data and generate novel combinations and possibilities.\\n\\nAI Agent Role: The AI agent acts as an insight engine, using its advanced perception and reasoning capabilities\\n\\nto identify patterns, connections, and opportunities that may not be apparent to the human. The AI\\'s role is to\\n\\naugment human creativity and problem-solving by providing new perspectives and possibilities, but relies on\\n\\nthe human to evaluate and act on these insights.\\n\\nUse for:\\n\\nPredicting market trends, identifying potential risks and opportunities, providing strategic foresight.\\n\\nIdentifying potential risks and opportunities in personal investments, providing insights on long-term career\\n\\nplanning, offering strategic guidance on personal growth and development.\\n\\nDo Not Use for:\\n\\nDirectly executing tasks, physically interacting with the environment, handling routine queries or\\n\\ntransactions.\\n\\nDirectly intervening in personal situations, providing hands-on support with tasks, offering intimate or\\n\\nemotional guidance.\\n\\nWayfinder\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nWayﬁnder\\n\\nHigh\\n\\nHigh\\n\\nHigh\\n\\nDescription: Your ultimate navigator and problem-solver, who masterfully guides you through the most\\n\\ncomplex and uncharted territories, drawing on advanced capabilities across all dimensions to help you ﬁnd\\n\\nyour way to success and achievement.\\n\\nArtiﬁciality\\n\\nPage 13\\n\\nhello@artiﬁciality.world\\n\\nHuman Role: The human acts as a high-level strategist and ethical governor, setting overall goals and values\\n\\nfor the AI to pursue. The human is responsible for deﬁning the strategic vision and ethical principles that guide\\n\\nthe AI\\'s actions, but relies on the AI to autonomously navigate complex environments and make decisions in\\n\\npursuit of these objectives.\\n\\nAI Agent Role: The AI agent acts as an autonomous cognitive agent, using its advanced perception, reasoning,\\n\\nand action capabilities to understand the world, make decisions, and take actions in sophisticated ways. The\\n\\nAI\\'s role is to operate with signiﬁcant autonomy and adaptability in pursuit of the objectives set by the human,\\n\\nwhile still remaining transparent and accountable to human oversight.\\n\\nUse for:\\n\\nManaging complex, multi-faceted projects, dynamically adapting to changing strategic priorities, providing\\n\\ncomprehensive and contextually aware decision support.\\n\\nManaging complex personal projects, dynamically adapting to changing life circumstances, providing\\n\\ncomprehensive and contextually aware personal decision support.\\n\\nDo Not Use for:\\n\\nHandling simple, routine tasks, providing direct physical assistance, dealing with highly emotional or\\n\\nsensitive situations.\\n\\nHandling simple daily routines, providing direct physical assistance, dealing with highly emotional or\\n\\nintimate personal matters.\\n\\nAgent Structures\\n\\nToday, AI agents are designed with three structures or\\n\\narchitectures: single agents, multiple agents in a\\n\\nvertical hierarchy, and multiple agents in a horizontal\\n\\nhierarchy. Single agent structures are generally less\\n\\ncomplex, as they focus on individual tasks and\\n\\nactions. Multi-agent structures are generally more\\n\\ncomplex, as they involve communication and\\n\\npotential emergent behaviors among the agents.\\n\\nThe organizational structure of multiple agents is\\n\\nproving to be important. Vertical structures offer\\n\\ncentralized control but can have information\\n\\nbottlenecks as all agents have to communicate through the “boss.” Horizontal structures offer more ﬂexibility but\\n\\ncan get weighed down by communication overhead and emergent behaviors. For instance, research has shown\\n\\nthat agents in horizontal architectures can spend 50% of their time giving orders to each other and exchanging\\n\\nniceties like saying “how are you?”\\n\\nPerhaps this is proof that LLMs trained on vast quantities of conversational text will learn to mimic water cooler\\n\\nchat. While chit-chat is important for human bonding, it’s likely just a waste of time and compute for machines.\\n\\nArtiﬁciality\\n\\nPage 14\\n\\nhello@artiﬁciality.world\\n\\nComparison of Single Agents vs Multi-Agents\\n\\nSingle Agents\\n\\nMulti-Agents\\n\\nComplexity\\n\\nGenerally less complex, as they focus on\\n\\nMore complex, as they involve\\n\\nindividual decision-making and\\n\\ncoordination, communication, and\\n\\nactions.\\n\\npotential emergent behaviors among\\n\\nmultiple agents.\\n\\nScalability\\n\\nLimited scalability, as they are designed\\n\\nHighly scalable, as they can distribute\\n\\nto handle speciﬁc tasks or\\n\\ntasks and decision-making among\\n\\nenvironments.\\n\\nmultiple agents.\\n\\nRobustness\\n\\nLess robust, as the failure of the single\\n\\nMore robust, as the system can continue\\n\\nagent can lead to system failure.\\n\\nto function even if some agents fail.\\n\\nFlexibility\\n\\nLess ﬂexible, as they are designed for\\n\\nMore ﬂexible, as they can adapt to\\n\\nspeciﬁc tasks and environments.\\n\\nchanges in the environment or goals\\n\\nthrough coordination and\\n\\nreorganization.\\n\\nCommunication\\n\\nNo communication with other agents,\\n\\nRequires communication and\\n\\nbut may interact with the environment\\n\\ncoordination mechanisms for agents to\\n\\nor human users.\\n\\nshare information and collaborate.\\n\\nAdvantages and Disadvantages of Single Agents, Vertical Multi-Agents, and Horizontal Multi- Agents\\n\\nSingle Agents\\n\\nVertical Multi-Agents\\n\\nHorizontal Multi-Agents\\n\\nAdvantages\\n\\nSimpler architecture • Easier to manage • Faster response times\\n\\nEffective delegation • Specialization • Effective coordination\\n\\nFlexibility • Parallel processing • Fault-tolerance\\n\\nDisadvantages\\n\\nLimited ﬂexibility • Limited adaptability • Single point of failure\\n\\nCommunication bottlenecks\\n\\nCascading failures • Limited adaptability\\n\\nCoordination complexity\\n\\nCoherance difficulty • Potential conﬂict\\n\\nArtiﬁciality\\n\\nPage 15\\n\\nhello@artiﬁciality.world\\n\\nCX to AX: Designing for Agents\\n\\nWhat happens when your customers start to adopt AI agents to “get things done,” including searching for new\\n\\nproducts, purchasing products, and interacting with customer service agents? Your customer service will no\\n\\nlonger solely consider human customers. Your marketing messages will no longer solely focus on human\\n\\nreception. Even your SEO might no longer solely focus on Google Search but on the countless AI agents that each\\n\\nindividual human uses to ﬁnd things on the Agentic Web.\\n\\nAs AI agents become more sophisticated, they may replace human customers in a wide range of interactions. This\\n\\nshift will require companies to fundamentally rethink their approach to customer experience design—shifting\\n\\nfrom customer experience (CX) to agent experience (AX).\\n\\nWhy would we want AI agents to handle these\\n\\ninteractions and experiences for us? First, agents are able to do things we might value: process vast\\n\\namounts of information, make objective decisions,\\n\\nand provide highly personalized experiences based on\\n\\ncomprehensive user data. Second, the future of the\\n\\ninternet (what we call the Agentic Web) will be\\n\\noverwhelmed by AI-generated content that humans\\n\\nwill not be able to process—it will only be readable by\\n\\nmachine. That means we will need AI agents to “read”\\n\\nthe internet for us since the internet will be made by\\n\\nmachines, for machines. Third, agents promise\\n\\nfreedom—freedom from the devices where they work\\n\\nfor us.\\n\\nThe adoption of AI agents is likely to begin with low-\\n\\ninteraction utilities, such as bill payments and simple\\n\\nservice inquiries. These transactions often involve\\n\\nstandardized processes and require minimal personal\\n\\ninput, making them well-suited for automation. As AI\\n\\nagents demonstrate their effectiveness and reliability\\n\\nin handling these tasks, consumers may become\\n\\nmore comfortable entrusting them with more complex\\n\\ninteractions. Imagine an AI agent that you use to purchase products on Amazon. Gone are the days of sifting\\n\\nthrough reviews and wondering if you might have missed something that’s important to you. An AI agent’s\\n\\ncapability to process and analyze vast amounts of data might be increasingly important as the quantity of\\n\\ncomments and reviews increases, written by AI.\\n\\nIt’s important to note here that we are unaware of authentication methods for AI agents that will be required for e-commerce companies, ﬁnancial institutions, and regulators to approve of AI agents conducting transactions.\\n\\nBut that gap is likely just a moment in time. As VeriSign’s authentication services grew out of the need to secure\\n\\nwebsite connections, services to authenticate AI agency will likely be developed too.\\n\\nArtiﬁciality\\n\\nPage 16\\n\\nhello@artiﬁciality.world\\n\\nIt’s not possible to know how far this transition to agentic customers will go and over what time frame. But, no\\n\\nmatter what, this future requires shifting focus from CS to AX. How might you design your company’s external\\n\\nexperience to appeal to AI agents that are acting on behalf of humans?\\n\\nDesigning for AX requires a fundamentally different approach than traditional CX design. Rather than treating\\n\\ninteractions as linear, predictable exchanges of information, AX design must account for the complex, emergent\\n\\ndynamics that arise when your organization’s agents interact with your customer’s agents. This means designing\\n\\ninterfaces and interactions that are not only intuitive and user-friendly for humans, but also for AI agents.\\n\\nDesigning for AI agents also means being prepared for unintended consequences and unexpected behaviors that\\n\\nmay arise from agent-agent interactions. This requires building in safeguards, monitoring mechanisms, and\\n\\ngovernance structures that can detect and respond to emergent risks or harms, while also enabling beneﬁcial\\n\\nforms of emergence and adaptation to occur. And this requires building entirely new safety mechanisms—for\\n\\nboth your organization and your customers.\\n\\nIt’s important to note that these design methods do not exist today. This is an important focus for Artiﬁciality. We\\n\\nworry that the early and easy answers like oversight, override, transparency, explainability, accountability, while\\n\\nessential, will not be sufficient as AI is able to handle more complexity with more agency. And that is happening\\n\\nfaster than anyone thought.\\n\\nAs a start, we offer a framework of seven design considerations, differentiated between a human (CX) and a\\n\\nmachine (AX).\\n\\nCX: Human\\n\\nAX: Machine\\n\\nInformation\\n\\nProcessing\\n\\nLimited attention span. • Susceptible to cognitive biases (e.g., recency bias, conﬁrmation bias), inﬂuenced by emotions and social factors.\\n\\nCan process vast amounts of information quickly, objective and data-driven decision-making.\\n\\nNot inﬂuenced by emotions or social factors.\\n\\nMay choose a product based on a recent ad they saw, even if it\\'s not the best option for their needs.\\n\\nAnalyzes thousands of product reviews and speciﬁcations to determine the best option based on the user\\'s needs and preferences.\\n\\nPersonalization • Appreciates personalized experiences\\n\\nExpects highly personalized\\n\\nbased on past behavior and preferences.\\n\\nexperiences based on comprehensive user data.\\n\\nMay respond positively to subtle cues\\n\\nRequires explicit and transparent\\n\\n\\n\\nand nudges. Is more likely to purchase a product if it\\'s recommended based on their previous purchases.\\n\\npersonalization.\\n\\nExpects product recommendations to be based on a comprehensive analysis of the user\\'s data, including browsing history, purchase history, and explicitly stated preferences.\\n\\nArtiﬁciality\\n\\nPage 17\\n\\nhello@artiﬁciality.world\\n\\nTrust &\\n\\nTransparency\\n\\nUser Interface\\n\\nPrivacy &\\n\\nSecurity\\n\\nFeedback &\\n\\nImprovement\\n\\nEthical\\n\\nConsiderations\\n\\nArtiﬁciality\\n\\nCX: Human\\n\\nBuilds trust through positive\\n\\nexperiences and brand reputation.\\n\\nMay not always understand or\\n\\nquestion underlying algorithms. • Trusts a well-known brand and may\\n\\nnot question how their recommendations are generated.\\n\\nResponds to visually appealing and\\n\\nintuitive interfaces.\\n\\nMay overlook minor usability issues. • Is more likely to engage with a website that has an attractive layout and easy navigation.\\n\\nConcerned about personal data privacy.\\n\\nExpects secure transactions and data handling.\\n\\nExpects their credit card information to be securely processed and stored when making an online purchase.\\n\\nProvides feedback through ratings, reviews, and customer support interactions.\\n\\nAdapts slowly to changes in preferences or needs\\n\\nLeaves a product review and rating based on their experience, which slowly inﬂuences the product\\'s overall rating and future development.\\n\\nMay be inﬂuenced by social norms\\n\\nand ethical considerations.\\n\\nExpects companies to adhere to ethical practices.\\n\\nExpects companies to adhere to ethical practices.\\n\\nMay avoid purchasing from a company known for unethical labor practices or environmental damage.\\n\\nPage 18\\n\\nAX: Machine\\n\\nRequires clear explanations of how recommendations are generated. • Demands transparency in data usage and decision-making processes.\\n\\nRequires a detailed explanation of the factors considered in generating a recommendation and how the user\\'s data is being utilized.\\n\\nPrioritizes efficiency and\\n\\nfunctionality over aesthetics. • Expects seamless integration with other AI systems and data sources. • Prioritizes a streamlined interface that allows for quick data input and retrieval, and seamless integration with other relevant AI systems.\\n\\nRequires strict adherence to privacy regulations and data protection standards.\\n\\nExpects advanced security measures to prevent unauthorized access or manipulation.\\n\\nRequires all data exchanges to be encrypted and adhere to the latest privacy regulations, with robust security measures in place to prevent data breaches or unauthorized access.\\n\\nContinuously learns and adapts based on real-time data and interactions.\\n\\nRequires dynamic and responsive systems that can quickly incorporate feedback.\\n\\nContinuously feeds real-time data on user interactions and preferences back into the system, allowing for rapid adaptation and improvement of the online experience.\\n\\nOperates based on predeﬁned ethical\\n\\nenforcement of ethical boundaries in AI decision-making.\\n\\nMakes decisions within predeﬁned ethical boundaries, such as avoiding products or services that violate human rights or environmental standards.\\n\\nhello@artiﬁciality.world\\n\\nAbout Artificiality\\n\\nExtending Minds Beyond\\n\\nArtiﬁciality is dedicated to unraveling AI\\'s impact on individuals, organizations, and society, blending expertise\\n\\nin ﬁelds ranging from cognitive science and complexity theory to philosophy and design. As artiﬁcial\\n\\nphilosophers and meta-researchers, we aim to make the philosophical more practical and the practical more\\n\\nphilosophical. Our hope is to extend human minds beyond our current experience through the collective\\n\\nintelligence of humans and machines.\\n\\nWe don’t endorse the utopian AI rhetoric. Yes, we believe that AI may have a positive impact on humanity. But our\\n\\nexcitement is balanced by our fear of Big Tech’s fantasy to make machines that can replace us. We strive to slow\\n\\nthe frantic pace of debates around AI, down-regulating the noise so you can see stories in context. We push and\\n\\npull on the assumptions that drive hype about the adoption of AI. And we ask questions about how AI changes\\n\\nknowledge, truth, belief, creativity, meaning, authenticity, and the overall human experience.\\n\\nAt Artiﬁciality, we believe in a new vision for human-machine collaboration in which AI serves as a Mind for our\\n\\nMinds, amplifying our creativity, agency, and wisdom. As AI systems emerge with the capability to perceive,\\n\\nreason, and act on their own, we must approach their development with a deep understanding of the human\\n\\nexperience. Our mission is to ensure that as we give machines more agency, we design AI minds to work for our\\n\\nhuman minds, enhancing our humanity rather than diminishing it.\\n\\nTo further our mission, we offer a range of services and resources. Our research publication cuts through the AI\\n\\nhype cycle, offering nuanced insights that dig deep into the current state and future possibilities of human-\\n\\nmachine collaboration. We discuss the science of humans and machines in plain English. And we share\\n\\naspirational ideas along with, at times, uncomfortably honest perspectives on AI\\'s limitations and the scenarios\\n\\nthat should concern humanity.\\n\\nOur strategic advisory services help organizations amplify their competitiveness by providing thought\\n\\nleadership, frameworks, and best practices that challenge mainstream narratives. We offer change management\\n\\nprograms to guide organizations through AI-driven complexity, embracing the uncertainties and emergent\\n\\nbehaviors inherent in human-machine systems. And our educational programs empower workforces to\\n\\ncollaborate with AI systems in ways that remain meaningfully human.\\n\\nJoin us on this wayﬁnding journey through the complexity of minds meeting machines. Subscribe, explore our\\n\\nservices, or contact us to learn more about how we can help you collaborate effectively with AI. With careful\\n\\nconsideration and a dash of creativity, the future may be human-centered yet.\\n\\nAdvisory Services\\n\\nIn the AI age, organizations face a daunting challenge: how to harness the potential of generative AI while\\n\\nnavigating the complexities of human-machine collaboration. At Artiﬁciality, we empower leaders to embrace\\n\\nthis challenge as an opportunity—an opportunity to reimagine the relationship between technology and\\n\\nhumanity.\\n\\nOur comprehensive suite of services guides you through every stage of your AI journey:\\n\\nAmplify Your Competitiveness\\n\\nArtiﬁciality\\n\\nPage 19\\n\\nhello@artiﬁciality.world\\n\\nStay ahead with cutting-edge AI research and unconventional thought leadership\\n\\nIdentify and capitalize on emerging AI-driven opportunities\\n\\nLeverage generative AI to innovate with new products and services\\n\\nMitigate threats from AI disruption to protect your market position\\n\\nBuild Your AI Capabilities\\n\\nCultivate AI skills and machine intuition that enhance human judgment\\n\\nFoster an AI-empowered culture aligned with your organization\\'s values\\n\\nEstablish cross-functional AI centers of excellence for expertise-sharing\\n\\nAttract and upskill top AI talent to drive your initiatives\\n\\nManage Complex Change\\n\\nImplement complex change methods for AI-driven transformation\\n\\nDevelop strategies to adapt to the emergent dynamics of human-AI collaboration\\n\\nManage cultural and ethical challenges of successful AI adoption\\n\\nBuild stakeholder trust through AI communication strategies\\n\\nAt Artiﬁciality, we believe that the key to unlocking the full potential of generative AI lies not in the technology\\n\\nitself, but in the human creativity, agency, and wisdom that guide its development and deployment. By designing\\n\\nAI systems that serve as a Mind for our Minds—amplifying our ability to learn, grow, and make meaning in the\\n\\nworld—we can create a future where technology is a catalyst for human ﬂourishing.\\n\\nContact us today to learn how Artiﬁciality can help you navigate the challenges and opportunities of the AI age\\n\\nwith conﬁdence and creativity. Together, let\\'s build a future where the collective intelligence of humans and\\n\\nmachines creates a world that extends our minds beyond our current experience.\\n\\nWho We Are\\n\\nHelen and Dave Edwards are a husband-and-wife team of technology executives and AI pioneers who founded\\n\\nArtiﬁciality. Their expertise blends insights from ﬁelds as diverse as cognitive science, complexity theory,\\n\\nphilosophy, and design, which they have applied in the AI space since 2012. In 2017, they sold their ﬁrst AI market\\n\\nresearch ﬁrm, Intelligentsia.ai, to Atlantic Media, continuing their work at Atlantic Media’s subsidiary Quartz.\\n\\nAt Artiﬁciality, Helen combines decades of experience innovating across major industries with a superpower for\\n\\nidentifying the next emergent scientiﬁc breakthrough that will impact our complex world of humans and\\n\\nmachines. She previously co-founded Intelligentsia.ai (which Atlantic Media acquired) and worked at Fonterra,\\n\\nMeridian Energy, Paciﬁc Gas & Electric, Quartz, and Transpower NZ.\\n\\nAt Artiﬁciality, Dave combines decades of experience distilling and advancing big technology trends with a\\n\\nsuperpower for understanding how technology, design, and capital will shape our future. He previously co-\\n\\nfounded Intelligentsia.ai (which Atlantic Media acquired) and worked at Apple, CRV, Macromedia, Morgan\\n\\nStanley, Quartz, and ThinkEquity.\\n\\nHelen and Dave are co-authors of Make Better Decisions: How to Improve Your Decision-Making in the Digital Age\\n\\nand the upcoming Mind for our Minds: A Human-Centered Design Philosophy for AI and Amplify: A Guide to the\\n\\nComplexity of Human-Machine Collaboration.\\n\\nArtiﬁciality\\n\\nPage 20\\n\\nhello@artiﬁciality.world')]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text Splitting"
      ],
      "metadata": {
        "id": "Ig_Tc37GEhV_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)\n",
        "docs = text_splitter.split_documents(data)"
      ],
      "metadata": {
        "id": "v28l9Y3lEdvH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVSqLB3GEds-",
        "outputId": "88bbb002-12ce-4540-f055-f9be07f0d73f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Agentic AI (aka AI Agents)\\n\\nThe future of AI is agentic.\\n\\nPublished: May 2024\\n\\nKey Points:\\n\\nAgentic AI: Agentic AI systems represent a signiﬁcant advancement in AI, characterized by their ability to\\n\\nperceive, reason, and act autonomously, extending human capabilities in unprecedented ways.\\n\\nDeﬁnitions and Perspectives: Various deﬁnitions from organizations like OpenAI, IBM, and Google\\n\\nDeepMind highlight the multifaceted nature of agentic AI, emphasizing goals, actions, and natural language\\n\\ninterfaces. At Artiﬁciality, we deﬁne agentic AI systems as those that can perceive, reason, and act with varying\\n\\ncomplexity to extend the human mind beyond our current experience.\\n\\nHistorical Context and Development: The concept of AI agency has evolved from early systems like IBM's\\n\\nDeep Blue to advanced platforms such as AutoGPT and LangChain, showcasing rapid advancements in the\\n\\nﬁeld.\\n\\nCore Components: Agentic AI systems are built on three main components:\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Perception: Utilizing advanced sensors and machine learning for environmental understanding.\\n\\nReasoning: Combining logical reasoning, probabilistic inference, and heuristic decision-making.\\n\\nAction: Executing tasks through pre-deﬁned behaviors, learned skills, and adaptive control.\\n\\nAgentic AI Personas: Different combinations of perception, reasoning, and action create varied AI personas,\\n\\nfrom simple aides to complex wayﬁnders, each suited for speciﬁc applications and levels of complexity.\\n\\nImpacts and Considerations: The deployment of agentic AI has the potential to signiﬁcantly impact daily\\n\\ntasks and strategic decision-making, but also raises concerns about control, ethical considerations, and\\n\\nunintended consequences.\\n\\nDesigning for AI Agents: The shift from customer experience (CX) to agent experience (AX) requires new\\n\\ndesign principles to accommodate AI agents, focusing on efficiency, transparency, and the ability to process vast\\n\\namounts of information.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Organizational Structures: Single-agent and multi-agent architectures (vertical and horizontal) each have\\n\\ndistinct advantages and challenges, inﬂuencing scalability, robustness, and ﬂexibility.\\n\\nArtiﬁciality\\n\\nPage 1\\n\\nhello@artiﬁciality.world\\n\\nIntroduction\\n\\nRecent developments in AI have given rise to a new class of systems known as agentic AI. These systems are\\n\\ncharacterized by their ability to perceive, reason, and act with varying levels of complexity, extending human\\n\\ncapabilities in unprecedented ways. While there isn’t a single deﬁnition of agentic AI—what is agreed upon is that\\n\\nagentic AI represents a signiﬁcant leap.\\n\\nOpenAI describes agentic AI systems as those that\\n\\n\"can pursue complex goals with limited direct\\n\\nsupervision.\" Researchers from Stanford, Microsoft,\\n\\nand UCLA characterize AI agents as \"a class of\\n\\ninteractive systems that can perceive visual stimuli,\\n\\nlanguage inputs, and other environmentally-\\n\\ngrounded data, and can produce meaningful actions.\"'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='IBM views AI agents as \"language model-powered\\n\\nentities able to plan and take actions to execute goals\\n\\nover multiple iterations,\" while Google DeepMind\\n\\ndeﬁnes advanced AI assistants as \"artiﬁcial agents\\n\\nwith natural language interfaces, whose function is to\\n\\nplan and execute sequences of actions on behalf of a\\n\\nuser—across one or more domains—in line with the\\n\\nuser\\'s expectations.\"\\n\\nAt Artiﬁciality, we deﬁne agentic AI systems as those\\n\\nthat can perceive, reason, and act with varying\\n\\ncomplexity to extend the human mind beyond our\\n\\ncurrent experience. This deﬁnition emphasizes the\\n\\ntrio of capabilities—perception, reasoning, and action\\n\\n—that we will use to frame various capabilities of AI\\n\\nagents later in this report.\\n\\nThe concept of AI agency AI is not entirely new, with\\n\\nroots tracing back to early systems like IBM\\'s Deep\\n\\nBlue chess-playing system in 1997 and the debut of\\n\\nvirtual assistants like Siri in 2011. However, recent\\n\\nyears have seen a rapid acceleration in the'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='development and deployment of increasingly\\n\\nsophisticated agentic AI systems. Early experiments\\n\\nlike AutoGPT and BabyAGI caught the AI\\n\\ncommunity’s attention while newer tools and\\n\\nplatforms like Brevian, CrewAI, LangChain, and\\n\\nQurrent provide the ability for individuals and\\n\\norganizations to develop their own AI agents.\\n\\nArtiﬁciality\\n\\nPage 2\\n\\nhello@artiﬁciality.world\\n\\nWe are focused on agentic AI because we believe a) it has the potential to have wide-ranging impacts on how we\\n\\nlive and work, b) these technologies have the potential to both empower and disempower, and c) some level of\\n\\nagency is the future of AI. Giving machines the agency to perceive, reason, and act to accomplish goals might be\\n\\nquite useful with everyday tasks. But there is also the possibility for AI agents to wreak havoc if left unchecked.\\n\\nSince everyone with a computer now has the ability to create agentic AI, there may be little to no ability to control\\n\\nhow and why these agents do what they do.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"In this report, we will explore the key components of agentic AI systems, including perception, reasoning, and\\n\\naction. We'll examine how these capabilities can be combined in various ways to create AI agents with different\\n\\nroles and personas, from simple task executors to complex decision support systems. We'll also consider the\\n\\nrealities of deploying agentic AI in real-world contexts, including the shift from designing for human customers\\n\\nto designing for AI agents themselves. Finally, we'll explore the challenges and opportunities of building multi-\\n\\nagent systems and the importance of assembling diverse, multidisciplinary teams to effectively develop and\\n\\nintegrate agentic AI technologies.\\n\\nIf you thought that LLMs changed everything (which they did), agentic AI will change everything even more, even\\n\\nif its full instantiation is many years away.\\n\\nArtiﬁciality\\n\\nPage 3\\n\\nhello@artiﬁciality.world\\n\\nThe Language of Agentic AI: A Caveat\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='“Can machines think? This should begin with deﬁnitions of the meaning of the terms ‘machine’ and “‘think.’”\\n\\n—Alan Turing\\n\\nThe rapid development of agentic AI systems has sparked a need for new metaphors and mental models to\\n\\ncomprehend their complexity. While these AI agents display intricate behaviors and interactions that surpass\\n\\nsimplistic notions of machines, they remain fundamentally distinct from biological life.\\n\\nWhen we describe advanced AI using terms traditionally reserved for living systems, we must recognize that this\\n\\nlinguistic mapping does not imply that AI possesses life or subjective experiences like that of humans. Instead,\\n\\nthis language of life, cognition, and behavior serves as a tool to help conceptualize the increasingly sophisticated\\n\\ncapabilities of AI, which emerge from complex informational dynamics and goal-directed, optimized functions.\\n\\nAs AI agents become more adaptive, interactive, and autonomous, we recognize they occupy a novel space on a'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='multidimensional continuum of possible complex systems, characterized by varying degrees of design versus\\n\\nemergence and mechanism versus cognition. Across this continuum, agentic AI may functionally mimic\\n\\nproperties of life—such as perception, reasoning, and action—through rational architectures that enable them to\\n\\npursue goals while interacting with complex environments.\\n\\nHowever, we must emphasize that this functional resemblance to living systems does not equate to AI possessing\\n\\nexistential choice, intentionality, phenomenological experience, or metacognition—qualities that characterize\\n\\nbiological cognition and remain qualitatively distinct from the computational processes of artiﬁcial agents.\\n\\nAs we attempt to describe these new AI capabilities, we face the challenge of developing more precise deﬁnitions\\n\\nand conceptual frameworks to reason about the novel design space of agentic systems. In this report, we lay the'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='groundwork for this task, aiming to maintain a balanced perspective that includes the use of appropriate\\n\\nmetaphors. By doing so, we hope to provide intellectual clarity as we shape a future in which artiﬁcial agents and\\n\\nliving cognitive systems interact in increasingly ﬂuid and synergistic ways.\\n\\nA key challenge is to develop more precise deﬁnitions and conceptual frameworks to reason about this novel\\n\\ndesign space of possible agentic systems, something we attempt to make a start on in this report. Our goal is to\\n\\nmaintain a balanced perspective—including use of appropriate metaphors—which will provide intellectual\\n\\nclarity as we shape a future in which artiﬁcial agents and living cognitive systems interact in increasingly ﬂuid\\n\\nand synergistic ways.\\n\\nArtiﬁciality\\n\\nPage 4\\n\\nhello@artiﬁciality.world\\n\\nUnderstanding Agentic AI\\n\\nAt the core of agentic AI systems are three key components: perception, reasoning, and action. Each of these'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"capabilities can be present in an AI agent to varying degrees of complexity, allowing for the creation of systems\\n\\nwith diverse skill sets and areas of specialization. By understanding how these components function and interact,\\n\\nwe can better grasp the potential and limitations of agentic AI.\\n\\nPerception\\n\\nUtilizes advanced sensors and and machine learning models to interpret and understand the environment.\\n\\nIncorporates spatial and visual reasoning capabilities to effectively navigate and interact with the physical\\n\\nworld.\\n\\nCombines intuitive pattern recognition with deliberate analysis to process and make sense of complex\\n\\nsensory information.\\n\\nAdapts and reﬁnes perceptual models based on experience and feedback, enabling the agent to handle novel\\n\\nand changing environments.\\n\\nPerception refers to an AI agent's ability to interpret and make sense of its environment. This involves taking in\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"data from various sensors or input channels and processing it to extract meaningful information. At a basic level,\\n\\nperceptual capabilities might include simple pattern recognition and feature extraction from structured data.\\n\\nHowever, as the complexity of an agent's perception increases, it can begin to handle more diverse, multi-modal\\n\\nsensory inputs and unstructured data.\\n\\nAdvanced perceptual capabilities in AI agents may include sophisticated spatial and visual reasoning, allowing\\n\\nthem to navigate and interact with the physical world in more natural and intuitive ways. These systems can also\\n\\ndemonstrate continuous adaptation and reﬁnement of their perceptual models, learning from experience to\\n\\nbetter handle novel situations and environments.\\n\\nAs the complexity of an AI agent's perception grows, it can give rise to emergent properties and behaviors. The\\n\\nsystem may begin to exhibit a more holistic and contextual understanding of its environment, integrating\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"multiple sources of information to form a coherent picture of the world around it.\\n\\nReasoning\\n\\nEmploys a combination of logical reasoning, probabilistic inference, and heuristic decision-making\\n\\nto solve problems and make decisions.\\n\\nBalances fast, intuitive processing (System 1) with slower, more deliberate reasoning (System 2)\\n\\nto efficiently handle a range of cognitive tasks.\\n\\nUtilizes spatial and visual representations to support planning, problem-solving, and creative thinking.\\n\\nArtiﬁciality\\n\\nPage 5\\n\\nhello@artiﬁciality.world\\n\\nIncorporates emergent properties arising from the interaction of various reasoning components, enabling the\\n\\nagent to exhibit intelligent behavior beyond the sum of its parts.\\n\\nConsiders the broader context and complex systems in which the agent operates, accounting for the\\n\\ninterdependencies and feedback loops that shape its reasoning processes.\\n\\nReasoning encompasses an AI agent's ability to process information, draw inferences, and make decisions based\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"on the data it has gathered through perception. At a basic level, this might involve predominantly heuristic-based\\n\\nreasoning with a limited ability to handle complex or ambiguous problems. Such systems may rely on narrow,\\n\\ndomain-speciﬁc planning and problem-solving strategies with minimal integration of spatial and visual\\n\\nreasoning.\\n\\nAs the complexity of an agent's reasoning increases, however, it can begin to demonstrate a more balanced\\n\\nintegration of intuitive and deliberate reasoning processes. These advanced systems can tackle ill-deﬁned, open-\\n\\nended problems by employing ﬂexible, adaptive problem-solving strategies that span multiple domains. They\\n\\nmay also incorporate seamless integration of spatial and visual reasoning, allowing them to manipulate and draw\\n\\ninsights from perceptual data in more sophisticated ways for more advanced planning and problem-solving..\\n\\nAt the highest levels of complexity, an AI agent's reasoning may begin to consider the broader context and\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='complex system dynamics in which it operates. This could involve explicit modeling of the interconnections and\\n\\nfeedback loops that shape the environment, as well as the ability to anticipate and adapt to emergent phenomena.\\n\\nThe result is the emergence of genuinely intelligent decision-making capabilities that can rival or even exceed\\n\\nhuman reasoning in certain domains.\\n\\nAction\\n\\nExecutes actions and interacts with the environment through a combination of pre-deﬁned behaviors,\\n\\nlearned skills, and adaptive control.\\n\\nEmploys spatial and visual reasoning to guide physical actions and manipulations in the world.\\n\\nBalances intuitive, reactive behaviors with deliberate, goal-directed actions to effectively\\n\\nrespond to different situations.\\n\\nLearns and adapts action strategies based on experience and feedback, allowing the agent\\n\\nto improve its performance over time.\\n\\nConsiders the potential emergent consequences of its actions on the broader system\\n\\nand environment in which it operates.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Action refers to an AI agent's ability to interact with its environment and effect change based on its perceptual\\n\\ninputs and reasoning processes. At a basic level, this might involve executing simple, pre-deﬁned action routines\\n\\nor motor skills with limited adaptability or learning from experience.\\n\\nAs the complexity of an agent's action capabilities increases, however, it can begin to demonstrate a more diverse\\n\\nand adaptive action repertoire. This might include sophisticated motor skills and behaviors that allow the agent\\n\\nto manipulate objects, navigate environments, and interact with humans in more natural and intuitive ways.\\n\\nArtiﬁciality\\n\\nPage 6\\n\\nhello@artiﬁciality.world\\n\\nAdvanced action capabilities may also involve deliberate, goal-directed control of actions, with the ability to plan\\n\\nand execute complex sequences of behaviors to achieve desired outcomes.\\n\\nCritically, the development of advanced action capabilities in AI agents often involves the integration of spatial\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='and visual reasoning. By leveraging perceptual data to guide physical interactions, these systems can\\n\\ndemonstrate a more embodied and situated form of intelligence that is grounded in the real world.\\n\\nArtiﬁciality\\n\\nPage 7\\n\\nhello@artiﬁciality.world\\n\\nPersonas\\n\\nThe three components of perception, reasoning, and action can be combined in various ways to create AI agents\\n\\nwith different capabilities and specializations. We can think of these combinations as deﬁning different agent\\n\\npersonas, each with its own strengths, limitations, and potential applications.\\n\\nFor example, an AI agent with low complexity in all three components might function as a simple aide or\\n\\nassistant, carrying out basic tasks with minimal autonomy or adaptability. Such a system might be well-suited to\\n\\nhandling routine data entry, scheduling, or customer service inquiries but would likely struggle with more open-\\n\\nended or ambiguous problems.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='In contrast, an agent with high complexity reasoning and action capabilities but low complexity perception\\n\\nmight function as a navigator or decision support system. Such an agent could analyze complex datasets,\\n\\ngenerate novel insights, and propose solutions to strategic problems but would rely on human input for situational awareness and contextual understanding.\\n\\nAt the highest end of the complexity spectrum, we might imagine an AI agent with advanced capabilities across\\n\\nall three components—a \"Wayﬁnder\" persona that can navigate uncharted territories, devise innovative solutions\\n\\nto complex problems, and adapt to changing circumstances with a high degree of autonomy. Such a system\\n\\nwould likely excel in domains requiring a combination of perceptual acuity, creative problem-solving, and\\n\\nsituated action, such as leading complex projects or guiding strategic decision-making.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='By understanding the interplay of perception, reasoning, and action in agentic AI systems, we can begin to map\\n\\nout the landscape of possible agent personas and skill sets. This, in turn, allows us to more effectively match AI\\n\\ncapabilities to real-world problems and applications, ensuring that we are deploying these powerful tools in ways\\n\\nthat maximize their potential while minimizing risks and unintended consequences.\\n\\nAgentic AI Personas\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nAide\\n\\nLow\\n\\nLow\\n\\nLow\\n\\nTrailblazer\\n\\nLow\\n\\nLow\\n\\nHigh\\n\\nNavigator\\n\\nLow\\n\\nHigh\\n\\nLow\\n\\nPathﬁnder\\n\\nLow\\n\\nHigh\\n\\nHigh\\n\\nLookout\\n\\nHigh\\n\\nLow\\n\\nLow\\n\\nVoyager\\n\\nHigh\\n\\nLow\\n\\nHigh\\n\\nOracle\\n\\nHigh\\n\\nHigh\\n\\nLow\\n\\nWayﬁnder\\n\\nHigh\\n\\nHigh\\n\\nHigh\\n\\nArtiﬁciality\\n\\nPage 8\\n\\nhello@artiﬁciality.world\\n\\nAide\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nAide\\n\\nLow\\n\\nLow\\n\\nLow\\n\\nDescription: Your dependable companion who carries your everyday burdens, guiding you through the basics\\n\\nwith patience and care.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Human Role: The human acts as a direct supervisor, providing step-by-step instructions and monitoring the\\n\\nAI's performance closely. The human is responsible for breaking down tasks into simple, executable steps and\\n\\nensuring the AI follows them accurately.\\n\\nAI Agent Role: The AI agent acts as a simple executor, following the human's explicit instructions without\\n\\nmuch autonomy or adaptation. The AI's role is to perform speciﬁc, well-deﬁned tasks as directed by the human.\\n\\nUse for:\\n\\nBasic data entry, simple customer service queries, routine scheduling tasks.\\n\\nManaging personal schedules, creating simple shopping lists, helping with basic travel arrangements.\\n\\nDo Not Use for:\\n\\nComplex data analysis, creative problem-solving, strategic decision-making.\\n\\nProviding in-depth life advice, handling complex personal ﬁnances, making important life decisions.\\n\\nTrailblazer\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nTrailblazer\\n\\nLow\\n\\nLow\\n\\nHigh\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Low\\n\\nLow\\n\\nHigh\\n\\nDescription: Your intrepid explorer who forges ahead, efficiently tackling repetitive tasks and clearing the\\n\\npath for your success.\\n\\nHuman Role: The human acts as a task manager, deﬁning speciﬁc goals and constraints for the AI to follow.\\n\\nThe human is responsible for setting up the AI's high-level objectives and monitoring its progress, but does not\\n\\nneed to provide as much direct instruction for the actual execution of tasks.\\n\\nAI Agent Role: The AI agent acts as a skilled operator, autonomously executing complex tasks and adapting to\\n\\nminor variations in the environment. The AI's role is to use its advanced action capabilities to achieve the\\n\\nspeciﬁc goals set by the human, while still relying on human guidance for overall strategy and decision-making.\\n\\nUse for:\\n\\nAutomated manufacturing processes, repetitive data cleanup, high-volume transaction processing.\\n\\nArtiﬁciality\\n\\nPage 9\\n\\nhello@artiﬁciality.world\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Automating repetitive household chores, managing routine bill payments, helping with basic tasks.\\n\\nDo Not Use for:\\n\\nHandling nuanced customer interactions, making judgment calls, providing strategic insights.\\n\\nProviding emotional support, offering nuanced advice on personal relationships, handling complex or\\n\\nsensitive family matters.\\n\\nNavigator\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nNavigator\\n\\nLow\\n\\nHigh\\n\\nLow\\n\\nDescription: Your skilled guide through complex information landscapes, who plots the best course through\\n\\ndata and ideas, steering you towards valuable insights and informed decisions.\\n\\nHuman Role: The human acts as a strategic partner, working with the AI to deﬁne high-level goals and\\n\\nstrategies. The human is responsible for providing domain expertise and ethical guidance, while relying on the\\n\\nAI to generate and evaluate potential solutions.\\n\\nAI Agent Role: The AI agent acts as a decision support system, using its advanced reasoning capabilities to'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"analyze data, generate insights, and propose solutions to the human. The AI's role is to augment human\\n\\ndecision-making by providing informed recommendations, while still relying on the human for ﬁnal approval\\n\\nand execution.\\n\\nUse for:\\n\\nAnalyzing complex datasets, providing data-driven insights, guiding decision-making processes.\\n\\nAnalyzing personal health data, providing insights on ﬁnancial planning, helping to research and compare\\n\\ncomplex products or services.\\n\\nDo Not Use for:\\n\\nPhysically executing tasks, directly interacting with customers, handling sensitive or emotional situations.\\n\\nDirectly intervening in personal situations, providing hands-on assistance with tasks, offering emotional\\n\\nguidance or support.\\n\\nArtiﬁciality\\n\\nPage 10\\n\\nhello@artiﬁciality.world\\n\\nPathfinder\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nPathﬁnder\\n\\nLow\\n\\nHigh\\n\\nHigh\\n\\nDescription: Your resourceful planner and problem-solver, who blazes trails through uncharted territories,\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"devising innovative strategies and solutions to help you reach your goals.\\n\\nHuman Role: The human acts as a high-level supervisor, setting overall objectives and constraints for the AI to\\n\\noperate within. The human is responsible for monitoring the AI's performance and ensuring its actions align\\n\\nwith human values and priorities, but does not need to be involved in the day-to-day decision-making and\\n\\nexecution of tasks.\\n\\nAI Agent Role: The AI agent acts as an autonomous problem-solver, using its advanced reasoning and action\\n\\ncapabilities to analyze complex situations, devise solutions, and execute them independently. The AI's role is to\\n\\ntake on signiﬁcant responsibility for achieving the objectives set by the human, while still remaining\\n\\naccountable to human oversight.\\n\\nUse for:\\n\\nOptimizing supply chain logistics, developing comprehensive project plans, creating innovative solutions to\\n\\ncomplex problems.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"complex problems.\\n\\nCreating detailed plans for personal projects, developing innovative solutions to complex personal challenges,\\n\\noptimizing personal productivity and goal achievement.\\n\\nDo Not Use for:\\n\\nGathering and interpreting raw data, handling physical tasks, providing emotional support.\\n\\nProviding direct physical assistance, handling intimate or sensitive personal matters, offering emotional\\n\\ncomfort or support.\\n\\nLookout\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nLookout\\n\\nHigh\\n\\nLow\\n\\nLow\\n\\nDescription: Your vigilant guardian who keeps a watchful eye on your environment, alerting you to potential\\n\\nrisks and anomalies and helping you stay on course.\\n\\nHuman Role: The human acts as an interpreter, translating the AI's advanced perceptual insights into\\n\\nactionable information. The human is responsible for making sense of the data and patterns identiﬁed by the AI\\n\\nand determining how to use this information to guide decision-making and action.\\n\\nArtiﬁciality\\n\\nPage 11\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Page 11\\n\\nhello@artiﬁciality.world\\n\\nAI Agent Role: The AI agent acts as a sensory processor, using its advanced perceptual capabilities to gather\\n\\nand analyze complex data from the environment. The AI's role is to provide the human with rich, detailed\\n\\ninsights about the world, but relies on the human to interpret this information and determine appropriate\\n\\nactions.\\n\\nUse for:\\n\\nMonitoring security cameras, detecting anomalies in large datasets, identifying potential quality control\\n\\nissues on production lines.\\n\\nMonitoring home security systems, detecting potential issues with personal devices or appliances,\\n\\nidentifying unusual patterns in personal health or ﬁnancial data.\\n\\nDo Not Use for:\\n\\nMaking complex decisions, physically responding to detected issues, providing strategic guidance.\\n\\nMaking judgment calls on personal matters, directly intervening in emergency situations, providing guidance\\n\\non sensitive personal issues.\\n\\nVoyager\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nVoyager\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Action\\n\\nVoyager\\n\\nHigh\\n\\nLow\\n\\nHigh\\n\\nDescription: Your fearless adventurer who boldly navigates complex, dynamic environments, adapting to new\\n\\nchallenges and discovering opportunities at every turn.\\n\\nHuman Role: The human acts as a supervisor and ethical governor, monitoring the AI's actions and ensuring\\n\\nthey align with human values and priorities. The human is responsible for setting overall objectives and\\n\\nconstraints, but relies on the AI to autonomously execute tasks and adapt to the environment.\\n\\nAI Agent Role: The AI agent acts as an adaptive automaton, using its advanced perception and action\\n\\ncapabilities to interact with the world in sophisticated ways. The AI's role is to autonomously execute tasks and\\n\\nrespond to changes in the environment, while still relying on the human for high-level guidance and ethical\\n\\noversight.\\n\\nUse for:\\n\\nAutonomous exploration of hazardous environments, dynamically adapting to changing conditions in\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='manufacturing processes, navigating complex virtual environments.\\n\\nExploring virtual worlds for entertainment, adapting to user preferences in personal digital assistants,\\n\\nnavigating complex online resources for personal research.\\n\\nDo Not Use for:\\n\\nProviding strategic direction, making ethical judgments, handling sensitive interpersonal situations.\\n\\nArtiﬁciality\\n\\nPage 12\\n\\nhello@artiﬁciality.world\\n\\nProviding guidance on major life decisions, handling sensitive personal information, offering emotional or\\n\\npsychological support.\\n\\nOracle\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nOracle\\n\\nHigh\\n\\nHigh\\n\\nLow\\n\\nDescription: Your wise and far-seeing guide, who reads the signs and interprets the mysteries, offering\\n\\nprophetic insights and illuminating the path ahead.\\n\\nHuman Role: The human acts as a collaborator and co-creator, working with the AI to generate new ideas and\\n\\nsolutions. The human is responsible for providing creative insight and domain expertise, while relying on the AI'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"to process complex data and generate novel combinations and possibilities.\\n\\nAI Agent Role: The AI agent acts as an insight engine, using its advanced perception and reasoning capabilities\\n\\nto identify patterns, connections, and opportunities that may not be apparent to the human. The AI's role is to\\n\\naugment human creativity and problem-solving by providing new perspectives and possibilities, but relies on\\n\\nthe human to evaluate and act on these insights.\\n\\nUse for:\\n\\nPredicting market trends, identifying potential risks and opportunities, providing strategic foresight.\\n\\nIdentifying potential risks and opportunities in personal investments, providing insights on long-term career\\n\\nplanning, offering strategic guidance on personal growth and development.\\n\\nDo Not Use for:\\n\\nDirectly executing tasks, physically interacting with the environment, handling routine queries or\\n\\ntransactions.\\n\\nDirectly intervening in personal situations, providing hands-on support with tasks, offering intimate or\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"emotional guidance.\\n\\nWayfinder\\n\\nPersona\\n\\nPerception\\n\\nReasoning\\n\\nAction\\n\\nWayﬁnder\\n\\nHigh\\n\\nHigh\\n\\nHigh\\n\\nDescription: Your ultimate navigator and problem-solver, who masterfully guides you through the most\\n\\ncomplex and uncharted territories, drawing on advanced capabilities across all dimensions to help you ﬁnd\\n\\nyour way to success and achievement.\\n\\nArtiﬁciality\\n\\nPage 13\\n\\nhello@artiﬁciality.world\\n\\nHuman Role: The human acts as a high-level strategist and ethical governor, setting overall goals and values\\n\\nfor the AI to pursue. The human is responsible for deﬁning the strategic vision and ethical principles that guide\\n\\nthe AI's actions, but relies on the AI to autonomously navigate complex environments and make decisions in\\n\\npursuit of these objectives.\\n\\nAI Agent Role: The AI agent acts as an autonomous cognitive agent, using its advanced perception, reasoning,\\n\\nand action capabilities to understand the world, make decisions, and take actions in sophisticated ways. The\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"AI's role is to operate with signiﬁcant autonomy and adaptability in pursuit of the objectives set by the human,\\n\\nwhile still remaining transparent and accountable to human oversight.\\n\\nUse for:\\n\\nManaging complex, multi-faceted projects, dynamically adapting to changing strategic priorities, providing\\n\\ncomprehensive and contextually aware decision support.\\n\\nManaging complex personal projects, dynamically adapting to changing life circumstances, providing\\n\\ncomprehensive and contextually aware personal decision support.\\n\\nDo Not Use for:\\n\\nHandling simple, routine tasks, providing direct physical assistance, dealing with highly emotional or\\n\\nsensitive situations.\\n\\nHandling simple daily routines, providing direct physical assistance, dealing with highly emotional or\\n\\nintimate personal matters.\\n\\nAgent Structures\\n\\nToday, AI agents are designed with three structures or\\n\\narchitectures: single agents, multiple agents in a\\n\\nvertical hierarchy, and multiple agents in a horizontal\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='hierarchy. Single agent structures are generally less\\n\\ncomplex, as they focus on individual tasks and\\n\\nactions. Multi-agent structures are generally more\\n\\ncomplex, as they involve communication and\\n\\npotential emergent behaviors among the agents.\\n\\nThe organizational structure of multiple agents is\\n\\nproving to be important. Vertical structures offer\\n\\ncentralized control but can have information\\n\\nbottlenecks as all agents have to communicate through the “boss.” Horizontal structures offer more ﬂexibility but\\n\\ncan get weighed down by communication overhead and emergent behaviors. For instance, research has shown\\n\\nthat agents in horizontal architectures can spend 50% of their time giving orders to each other and exchanging\\n\\nniceties like saying “how are you?”\\n\\nPerhaps this is proof that LLMs trained on vast quantities of conversational text will learn to mimic water cooler\\n\\nchat. While chit-chat is important for human bonding, it’s likely just a waste of time and compute for machines.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Artiﬁciality\\n\\nPage 14\\n\\nhello@artiﬁciality.world\\n\\nComparison of Single Agents vs Multi-Agents\\n\\nSingle Agents\\n\\nMulti-Agents\\n\\nComplexity\\n\\nGenerally less complex, as they focus on\\n\\nMore complex, as they involve\\n\\nindividual decision-making and\\n\\ncoordination, communication, and\\n\\nactions.\\n\\npotential emergent behaviors among\\n\\nmultiple agents.\\n\\nScalability\\n\\nLimited scalability, as they are designed\\n\\nHighly scalable, as they can distribute\\n\\nto handle speciﬁc tasks or\\n\\ntasks and decision-making among\\n\\nenvironments.\\n\\nmultiple agents.\\n\\nRobustness\\n\\nLess robust, as the failure of the single\\n\\nMore robust, as the system can continue\\n\\nagent can lead to system failure.\\n\\nto function even if some agents fail.\\n\\nFlexibility\\n\\nLess ﬂexible, as they are designed for\\n\\nMore ﬂexible, as they can adapt to\\n\\nspeciﬁc tasks and environments.\\n\\nchanges in the environment or goals\\n\\nthrough coordination and\\n\\nreorganization.\\n\\nCommunication\\n\\nNo communication with other agents,\\n\\nRequires communication and'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='but may interact with the environment\\n\\ncoordination mechanisms for agents to\\n\\nor human users.\\n\\nshare information and collaborate.\\n\\nAdvantages and Disadvantages of Single Agents, Vertical Multi-Agents, and Horizontal Multi- Agents\\n\\nSingle Agents\\n\\nVertical Multi-Agents\\n\\nHorizontal Multi-Agents\\n\\nAdvantages\\n\\nSimpler architecture • Easier to manage • Faster response times\\n\\nEffective delegation • Specialization • Effective coordination\\n\\nFlexibility • Parallel processing • Fault-tolerance\\n\\nDisadvantages\\n\\nLimited ﬂexibility • Limited adaptability • Single point of failure\\n\\nCommunication bottlenecks\\n\\nCascading failures • Limited adaptability\\n\\nCoordination complexity\\n\\nCoherance difficulty • Potential conﬂict\\n\\nArtiﬁciality\\n\\nPage 15\\n\\nhello@artiﬁciality.world\\n\\nCX to AX: Designing for Agents\\n\\nWhat happens when your customers start to adopt AI agents to “get things done,” including searching for new'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='products, purchasing products, and interacting with customer service agents? Your customer service will no\\n\\nlonger solely consider human customers. Your marketing messages will no longer solely focus on human\\n\\nreception. Even your SEO might no longer solely focus on Google Search but on the countless AI agents that each\\n\\nindividual human uses to ﬁnd things on the Agentic Web.\\n\\nAs AI agents become more sophisticated, they may replace human customers in a wide range of interactions. This\\n\\nshift will require companies to fundamentally rethink their approach to customer experience design—shifting\\n\\nfrom customer experience (CX) to agent experience (AX).\\n\\nWhy would we want AI agents to handle these\\n\\ninteractions and experiences for us? First, agents are able to do things we might value: process vast\\n\\namounts of information, make objective decisions,\\n\\nand provide highly personalized experiences based on\\n\\ncomprehensive user data. Second, the future of the'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='internet (what we call the Agentic Web) will be\\n\\noverwhelmed by AI-generated content that humans\\n\\nwill not be able to process—it will only be readable by\\n\\nmachine. That means we will need AI agents to “read”\\n\\nthe internet for us since the internet will be made by\\n\\nmachines, for machines. Third, agents promise\\n\\nfreedom—freedom from the devices where they work\\n\\nfor us.\\n\\nThe adoption of AI agents is likely to begin with low-\\n\\ninteraction utilities, such as bill payments and simple\\n\\nservice inquiries. These transactions often involve\\n\\nstandardized processes and require minimal personal\\n\\ninput, making them well-suited for automation. As AI\\n\\nagents demonstrate their effectiveness and reliability\\n\\nin handling these tasks, consumers may become\\n\\nmore comfortable entrusting them with more complex\\n\\ninteractions. Imagine an AI agent that you use to purchase products on Amazon. Gone are the days of sifting'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='through reviews and wondering if you might have missed something that’s important to you. An AI agent’s\\n\\ncapability to process and analyze vast amounts of data might be increasingly important as the quantity of\\n\\ncomments and reviews increases, written by AI.\\n\\nIt’s important to note here that we are unaware of authentication methods for AI agents that will be required for e-commerce companies, ﬁnancial institutions, and regulators to approve of AI agents conducting transactions.\\n\\nBut that gap is likely just a moment in time. As VeriSign’s authentication services grew out of the need to secure\\n\\nwebsite connections, services to authenticate AI agency will likely be developed too.\\n\\nArtiﬁciality\\n\\nPage 16\\n\\nhello@artiﬁciality.world\\n\\nIt’s not possible to know how far this transition to agentic customers will go and over what time frame. But, no\\n\\nmatter what, this future requires shifting focus from CS to AX. How might you design your company’s external'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='experience to appeal to AI agents that are acting on behalf of humans?\\n\\nDesigning for AX requires a fundamentally different approach than traditional CX design. Rather than treating\\n\\ninteractions as linear, predictable exchanges of information, AX design must account for the complex, emergent\\n\\ndynamics that arise when your organization’s agents interact with your customer’s agents. This means designing\\n\\ninterfaces and interactions that are not only intuitive and user-friendly for humans, but also for AI agents.\\n\\nDesigning for AI agents also means being prepared for unintended consequences and unexpected behaviors that\\n\\nmay arise from agent-agent interactions. This requires building in safeguards, monitoring mechanisms, and\\n\\ngovernance structures that can detect and respond to emergent risks or harms, while also enabling beneﬁcial\\n\\nforms of emergence and adaptation to occur. And this requires building entirely new safety mechanisms—for\\n\\nboth your organization and your customers.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"It’s important to note that these design methods do not exist today. This is an important focus for Artiﬁciality. We\\n\\nworry that the early and easy answers like oversight, override, transparency, explainability, accountability, while\\n\\nessential, will not be sufficient as AI is able to handle more complexity with more agency. And that is happening\\n\\nfaster than anyone thought.\\n\\nAs a start, we offer a framework of seven design considerations, differentiated between a human (CX) and a\\n\\nmachine (AX).\\n\\nCX: Human\\n\\nAX: Machine\\n\\nInformation\\n\\nProcessing\\n\\nLimited attention span. • Susceptible to cognitive biases (e.g., recency bias, conﬁrmation bias), inﬂuenced by emotions and social factors.\\n\\nCan process vast amounts of information quickly, objective and data-driven decision-making.\\n\\nNot inﬂuenced by emotions or social factors.\\n\\nMay choose a product based on a recent ad they saw, even if it's not the best option for their needs.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Analyzes thousands of product reviews and speciﬁcations to determine the best option based on the user's needs and preferences.\\n\\nPersonalization • Appreciates personalized experiences\\n\\nExpects highly personalized\\n\\nbased on past behavior and preferences.\\n\\nexperiences based on comprehensive user data.\\n\\nMay respond positively to subtle cues\\n\\nRequires explicit and transparent\\n\\n\\n\\nand nudges. Is more likely to purchase a product if it's recommended based on their previous purchases.\\n\\npersonalization.\\n\\nExpects product recommendations to be based on a comprehensive analysis of the user's data, including browsing history, purchase history, and explicitly stated preferences.\\n\\nArtiﬁciality\\n\\nPage 17\\n\\nhello@artiﬁciality.world\\n\\nTrust &\\n\\nTransparency\\n\\nUser Interface\\n\\nPrivacy &\\n\\nSecurity\\n\\nFeedback &\\n\\nImprovement\\n\\nEthical\\n\\nConsiderations\\n\\nArtiﬁciality\\n\\nCX: Human\\n\\nBuilds trust through positive\\n\\nexperiences and brand reputation.\\n\\nMay not always understand or\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"question underlying algorithms. • Trusts a well-known brand and may\\n\\nnot question how their recommendations are generated.\\n\\nResponds to visually appealing and\\n\\nintuitive interfaces.\\n\\nMay overlook minor usability issues. • Is more likely to engage with a website that has an attractive layout and easy navigation.\\n\\nConcerned about personal data privacy.\\n\\nExpects secure transactions and data handling.\\n\\nExpects their credit card information to be securely processed and stored when making an online purchase.\\n\\nProvides feedback through ratings, reviews, and customer support interactions.\\n\\nAdapts slowly to changes in preferences or needs\\n\\nLeaves a product review and rating based on their experience, which slowly inﬂuences the product's overall rating and future development.\\n\\nMay be inﬂuenced by social norms\\n\\nand ethical considerations.\\n\\nExpects companies to adhere to ethical practices.\\n\\nExpects companies to adhere to ethical practices.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"May avoid purchasing from a company known for unethical labor practices or environmental damage.\\n\\nPage 18\\n\\nAX: Machine\\n\\nRequires clear explanations of how recommendations are generated. • Demands transparency in data usage and decision-making processes.\\n\\nRequires a detailed explanation of the factors considered in generating a recommendation and how the user's data is being utilized.\\n\\nPrioritizes efficiency and\\n\\nfunctionality over aesthetics. • Expects seamless integration with other AI systems and data sources. • Prioritizes a streamlined interface that allows for quick data input and retrieval, and seamless integration with other relevant AI systems.\\n\\nRequires strict adherence to privacy regulations and data protection standards.\\n\\nExpects advanced security measures to prevent unauthorized access or manipulation.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Requires all data exchanges to be encrypted and adhere to the latest privacy regulations, with robust security measures in place to prevent data breaches or unauthorized access.\\n\\nContinuously learns and adapts based on real-time data and interactions.\\n\\nRequires dynamic and responsive systems that can quickly incorporate feedback.\\n\\nContinuously feeds real-time data on user interactions and preferences back into the system, allowing for rapid adaptation and improvement of the online experience.\\n\\nOperates based on predeﬁned ethical\\n\\nenforcement of ethical boundaries in AI decision-making.\\n\\nMakes decisions within predeﬁned ethical boundaries, such as avoiding products or services that violate human rights or environmental standards.\\n\\nhello@artiﬁciality.world\\n\\nAbout Artificiality\\n\\nExtending Minds Beyond\\n\\nArtiﬁciality is dedicated to unraveling AI's impact on individuals, organizations, and society, blending expertise\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='in ﬁelds ranging from cognitive science and complexity theory to philosophy and design. As artiﬁcial\\n\\nphilosophers and meta-researchers, we aim to make the philosophical more practical and the practical more\\n\\nphilosophical. Our hope is to extend human minds beyond our current experience through the collective\\n\\nintelligence of humans and machines.\\n\\nWe don’t endorse the utopian AI rhetoric. Yes, we believe that AI may have a positive impact on humanity. But our\\n\\nexcitement is balanced by our fear of Big Tech’s fantasy to make machines that can replace us. We strive to slow\\n\\nthe frantic pace of debates around AI, down-regulating the noise so you can see stories in context. We push and\\n\\npull on the assumptions that drive hype about the adoption of AI. And we ask questions about how AI changes\\n\\nknowledge, truth, belief, creativity, meaning, authenticity, and the overall human experience.'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"At Artiﬁciality, we believe in a new vision for human-machine collaboration in which AI serves as a Mind for our\\n\\nMinds, amplifying our creativity, agency, and wisdom. As AI systems emerge with the capability to perceive,\\n\\nreason, and act on their own, we must approach their development with a deep understanding of the human\\n\\nexperience. Our mission is to ensure that as we give machines more agency, we design AI minds to work for our\\n\\nhuman minds, enhancing our humanity rather than diminishing it.\\n\\nTo further our mission, we offer a range of services and resources. Our research publication cuts through the AI\\n\\nhype cycle, offering nuanced insights that dig deep into the current state and future possibilities of human-\\n\\nmachine collaboration. We discuss the science of humans and machines in plain English. And we share\\n\\naspirational ideas along with, at times, uncomfortably honest perspectives on AI's limitations and the scenarios\\n\\nthat should concern humanity.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Our strategic advisory services help organizations amplify their competitiveness by providing thought\\n\\nleadership, frameworks, and best practices that challenge mainstream narratives. We offer change management\\n\\nprograms to guide organizations through AI-driven complexity, embracing the uncertainties and emergent\\n\\nbehaviors inherent in human-machine systems. And our educational programs empower workforces to\\n\\ncollaborate with AI systems in ways that remain meaningfully human.\\n\\nJoin us on this wayﬁnding journey through the complexity of minds meeting machines. Subscribe, explore our\\n\\nservices, or contact us to learn more about how we can help you collaborate effectively with AI. With careful\\n\\nconsideration and a dash of creativity, the future may be human-centered yet.\\n\\nAdvisory Services\\n\\nIn the AI age, organizations face a daunting challenge: how to harness the potential of generative AI while'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"navigating the complexities of human-machine collaboration. At Artiﬁciality, we empower leaders to embrace\\n\\nthis challenge as an opportunity—an opportunity to reimagine the relationship between technology and\\n\\nhumanity.\\n\\nOur comprehensive suite of services guides you through every stage of your AI journey:\\n\\nAmplify Your Competitiveness\\n\\nArtiﬁciality\\n\\nPage 19\\n\\nhello@artiﬁciality.world\\n\\nStay ahead with cutting-edge AI research and unconventional thought leadership\\n\\nIdentify and capitalize on emerging AI-driven opportunities\\n\\nLeverage generative AI to innovate with new products and services\\n\\nMitigate threats from AI disruption to protect your market position\\n\\nBuild Your AI Capabilities\\n\\nCultivate AI skills and machine intuition that enhance human judgment\\n\\nFoster an AI-empowered culture aligned with your organization's values\\n\\nEstablish cross-functional AI centers of excellence for expertise-sharing\\n\\nAttract and upskill top AI talent to drive your initiatives\\n\\nManage Complex Change\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content=\"Implement complex change methods for AI-driven transformation\\n\\nDevelop strategies to adapt to the emergent dynamics of human-AI collaboration\\n\\nManage cultural and ethical challenges of successful AI adoption\\n\\nBuild stakeholder trust through AI communication strategies\\n\\nAt Artiﬁciality, we believe that the key to unlocking the full potential of generative AI lies not in the technology\\n\\nitself, but in the human creativity, agency, and wisdom that guide its development and deployment. By designing\\n\\nAI systems that serve as a Mind for our Minds—amplifying our ability to learn, grow, and make meaning in the\\n\\nworld—we can create a future where technology is a catalyst for human ﬂourishing.\\n\\nContact us today to learn how Artiﬁciality can help you navigate the challenges and opportunities of the AI age\\n\\nwith conﬁdence and creativity. Together, let's build a future where the collective intelligence of humans and\\n\\nmachines creates a world that extends our minds beyond our current experience.\"),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Who We Are\\n\\nHelen and Dave Edwards are a husband-and-wife team of technology executives and AI pioneers who founded\\n\\nArtiﬁciality. Their expertise blends insights from ﬁelds as diverse as cognitive science, complexity theory,\\n\\nphilosophy, and design, which they have applied in the AI space since 2012. In 2017, they sold their ﬁrst AI market\\n\\nresearch ﬁrm, Intelligentsia.ai, to Atlantic Media, continuing their work at Atlantic Media’s subsidiary Quartz.\\n\\nAt Artiﬁciality, Helen combines decades of experience innovating across major industries with a superpower for\\n\\nidentifying the next emergent scientiﬁc breakthrough that will impact our complex world of humans and\\n\\nmachines. She previously co-founded Intelligentsia.ai (which Atlantic Media acquired) and worked at Fonterra,\\n\\nMeridian Energy, Paciﬁc Gas & Electric, Quartz, and Transpower NZ.\\n\\nAt Artiﬁciality, Dave combines decades of experience distilling and advancing big technology trends with a'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='superpower for understanding how technology, design, and capital will shape our future. He previously co-\\n\\nfounded Intelligentsia.ai (which Atlantic Media acquired) and worked at Apple, CRV, Macromedia, Morgan\\n\\nStanley, Quartz, and ThinkEquity.\\n\\nHelen and Dave are co-authors of Make Better Decisions: How to Improve Your Decision-Making in the Digital Age\\n\\nand the upcoming Mind for our Minds: A Human-Centered Design Philosophy for AI and Amplify: A Guide to the\\n\\nComplexity of Human-Machine Collaboration.\\n\\nArtiﬁciality\\n\\nPage 20\\n\\nhello@artiﬁciality.world')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0du36H1EmR9",
        "outputId": "7b18af55-3fb8-4d8f-ebd3-41970818c235"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Embedding Convertion"
      ],
      "metadata": {
        "id": "KAWoYPCCEr-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2Hhmkf-KuwV",
        "outputId": "7e5ec853-af00-4f83-8c1c-1d24a710afed"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.27.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.67.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.12.14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain-huggingface"
      ],
      "metadata": {
        "id": "5zkY8vE_LDg2"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "embedding = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")"
      ],
      "metadata": {
        "id": "U13DhBHdEoLf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478,
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            "07a77a6f97ea4fb48de656bd1dd73847",
            "356d21e568a7467cb1ff292306e41299",
            "958e59dd4faa4e689de6be57a49999ed",
            "46ae53c9d8dc4075b259d8fba55dfcea",
            "b9dd4c2228fd4c918430d18b8822e428",
            "5f083e39a4bf442b9ebe4fab3a52d296",
            "30ad711868d945ad8d115e96bcb9de3a",
            "01b78c4027ba49c7abc2181ca4963898",
            "bbf5145428da4550a91da1e3dd466bb5",
            "87f526f819334fb4a32791ce863f4ebf",
            "cd35a547a2784a3882a65741ed89e31b",
            "744401cd5e724ad78b33c034ce55fdcb",
            "778e0cbe3bb04611a60a78e8e907ea98",
            "f65a8e5bcda64d4093462fecd1785a2d",
            "7dc7a7e3fd1d4ae2bf54fd5c106ee8c8",
            "ae0f1c58564c4a5e83d739c04d181d0e",
            "af87286a12dc40618799a7608facd482"
          ]
        },
        "outputId": "501a44f3-45d8-4a60-8534-0fec25a03b28"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
            "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
            "You are not authenticated with the Hugging Face Hub in this notebook.\n",
            "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "model_id": "3a15ae1e366d4446bfed6a5b5927a684"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7a3b2a2783d24527bfcc9b7e6a0adfe5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d2ec78ecb0e471281dc31c34364d4eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02238bb21e094545b273f5fe788a513c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "573c158e0043457e89007b57ad088cf6"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5055aa479b0446e94e2691a556a6b63"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "5e580399c7fa41448897dbec45171500"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "model_id": "77672a1c648f42f2a68a15da264b7be0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "30ad711868d945ad8d115e96bcb9de3a"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = embedding"
      ],
      "metadata": {
        "id": "rxveCbNmbaar"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vector Database Storage"
      ],
      "metadata": {
        "id": "Pa6pqWXVE5b2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -Uqq langchain-weaviate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0UVImhbLnBP",
        "outputId": "d695ab76-a99c-454c-a4dc-efe6cdff2383"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/519.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m481.3/519.1 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.1/519.1 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import weaviate\n",
        "from langchain_weaviate.vectorstores import WeaviateVectorStore\n",
        "from weaviate.classes.init import Auth\n",
        "import os\n",
        "\n",
        "#Connect to weaviate Cluster\n",
        "WEAVIATE_URL = WEAVIATE_CLUSTER\n",
        "\n",
        "huggingface_key = userdata.get(\"HF_TOKEN\")\n",
        "client = weaviate.connect_to_weaviate_cloud(\n",
        "    cluster_url = WEAVIATE_URL,\n",
        "    auth_credentials=Auth.api_key(WEAVIATE_API_KEY),      # `weaviate_key`: your Weaviate API key\n",
        "    headers = {\"X-HuggingFace-Api-Key\": huggingface_key},\n",
        ")"
      ],
      "metadata": {
        "id": "gu-4tnGSE3H2"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "client.is_ready()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLrjVzwkE3Fv",
        "outputId": "ab2e71d3-ab91-4900-ecf0-00f76990b8ef"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db = WeaviateVectorStore.from_documents(docs, embeddings, client=client)"
      ],
      "metadata": {
        "id": "RBzMV0JhbPiD"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Similarity Measurement"
      ],
      "metadata": {
        "id": "qMNy5RQ9Gjn1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"what is a vertical AI Agent?\"\n",
        "\n",
        "# retrieve text related to the query\n",
        "docs = db.similarity_search(query, k=2)"
      ],
      "metadata": {
        "id": "BNFmkvRsGDVI"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJ8RTLX0GrFe",
        "outputId": "26bd595d-143e-4c12-c1ee-381b35d6bdc6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='Organizational Structures: Single-agent and multi-agent architectures (vertical and horizontal) each have\\n\\ndistinct advantages and challenges, inﬂuencing scalability, robustness, and ﬂexibility.\\n\\nArtiﬁciality\\n\\nPage 1\\n\\nhello@artiﬁciality.world\\n\\nIntroduction\\n\\nRecent developments in AI have given rise to a new class of systems known as agentic AI. These systems are\\n\\ncharacterized by their ability to perceive, reason, and act with varying levels of complexity, extending human\\n\\ncapabilities in unprecedented ways. While there isn’t a single deﬁnition of agentic AI—what is agreed upon is that\\n\\nagentic AI represents a signiﬁcant leap.\\n\\nOpenAI describes agentic AI systems as those that\\n\\n\"can pursue complex goals with limited direct\\n\\nsupervision.\" Researchers from Stanford, Microsoft,\\n\\nand UCLA characterize AI agents as \"a class of\\n\\ninteractive systems that can perceive visual stimuli,\\n\\nlanguage inputs, and other environmentally-\\n\\ngrounded data, and can produce meaningful actions.\"'),\n",
              " Document(metadata={'source': 'data/Agentic-AI.pdf'}, page_content='but may interact with the environment\\n\\ncoordination mechanisms for agents to\\n\\nor human users.\\n\\nshare information and collaborate.\\n\\nAdvantages and Disadvantages of Single Agents, Vertical Multi-Agents, and Horizontal Multi- Agents\\n\\nSingle Agents\\n\\nVertical Multi-Agents\\n\\nHorizontal Multi-Agents\\n\\nAdvantages\\n\\nSimpler architecture • Easier to manage • Faster response times\\n\\nEffective delegation • Specialization • Effective coordination\\n\\nFlexibility • Parallel processing • Fault-tolerance\\n\\nDisadvantages\\n\\nLimited ﬂexibility • Limited adaptability • Single point of failure\\n\\nCommunication bottlenecks\\n\\nCascading failures • Limited adaptability\\n\\nCoordination complexity\\n\\nCoherance difficulty • Potential conﬂict\\n\\nArtiﬁciality\\n\\nPage 15\\n\\nhello@artiﬁciality.world\\n\\nCX to AX: Designing for Agents\\n\\nWhat happens when your customers start to adopt AI agents to “get things done,” including searching for new')]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "template = \"\"\"Answer the question based only on the following context:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\"\"\"\n",
        "\n",
        "# The prompt expects input with keys for \"context\" and \"question\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "retriever=db.as_retriever()\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "\n",
        "qa_chain = (\n",
        "    {\n",
        "        \"context\": retriever | format_docs,\n",
        "        \"question\": RunnablePassthrough(),\n",
        "    }\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")\n",
        "\n",
        "qa_chain.invoke(\"what is a vertical AI Agent?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "B3xYdEmPRz4-",
        "outputId": "cc18ff6e-06e2-4d3f-f89a-163fa76a67f7"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Based on the provided text, a vertical AI agent is a multi-agent system with a hierarchical structure.  It offers effective delegation, specialization, and effective coordination as advantages.  However, disadvantages include communication bottlenecks, cascading failures, and limited adaptability.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    }
  ]
}