1_lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to the start of your adventure in Agentic AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Are you ready for action??</h2>\n",
    "            <span style=\"color:#ff7800;\">Have you completed all the setup steps in the <a href=\"../setup/\">setup</a> folder?<br/>\n",
    "            Have you read the <a href=\"../README.md\">README</a>? Many common questions are answered here!<br/>\n",
    "            Have you checked out the guides in the <a href=\"../guides/01_intro.ipynb\">guides</a> folder?<br/>\n",
    "            Well in that case, you're ready!!\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/tools.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#00bfff;\">This code is a live resource - keep an eye out for my updates</h2>\n",
    "            <span style=\"color:#00bfff;\">I push updates regularly. As people ask questions or have problems, I add more examples and improve explanations. As a result, the code below might not be identical to the videos, as I've added more steps and better comments. Consider this like an interactive book that accompanies the lectures.<br/><br/>\n",
    "            I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And please do remember to contact me if I can help\n",
    "\n",
    "And I love to connect: https://www.linkedin.com/in/eddonner/\n",
    "\n",
    "\n",
    "### New to Notebooks like this one? Head over to the guides folder!\n",
    "\n",
    "Just to check you've already added the Python and Jupyter extensions to Cursor, if not already installed:\n",
    "- Open extensions (View >> extensions)\n",
    "- Search for python, and when the results show, click on the ms-python one, and Install it if not already installed\n",
    "- Search for jupyter, and when the results show, click on the Microsoft one, and Install it if not already installed  \n",
    "Then View >> Explorer to bring back the File Explorer.\n",
    "\n",
    "And then:\n",
    "1. Click where it says \"Select Kernel\" near the top right, and select the option called `.venv (Python 3.12.9)` or similar, which should be the first choice or the most prominent choice. You may need to choose \"Python Environments\" first.\n",
    "2. Click in each \"cell\" below, starting with the cell immediately below this text, and press Shift+Enter to run\n",
    "3. Enjoy!\n",
    "\n",
    "After you click \"Select Kernel\", if there is no option like `.venv (Python 3.12.9)` then please do the following:  \n",
    "1. On Mac: From the Cursor menu, choose Settings >> VS Code Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`);  \n",
    "On Windows PC: From the File menu, choose Preferences >> VS Code Settings(NOTE: be sure to select `VSCode Settings` not `Cursor Settings`)  \n",
    "2. In the Settings search bar, type \"venv\"  \n",
    "3. In the field \"Path to folder with a list of Virtual Environments\" put the path to the project root, like C:\\Users\\username\\projects\\agents (on a Windows PC) or /Users/username/projects/agents (on Mac or Linux).  \n",
    "And then try again.\n",
    "\n",
    "Having problems with missing Python versions in that list? Have you ever used Anaconda before? It might be interferring. Quit Cursor, bring up a new command line, and make sure that your Anaconda environment is deactivated:    \n",
    "`conda deactivate`  \n",
    "And if you still have any problems with conda and python versions, it's possible that you will need to run this too:  \n",
    "`conda config --set auto_activate_base false`  \n",
    "and then from within the Agents directory, you should be able to run `uv python list` and see the Python 3.12 version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's do an import. If you get an Import Error, double check that your Kernel is correct..\n",
    "\n",
    "from dotenv import load_dotenv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next it's time to load the API keys into environment variables\n",
    "# If this returns false, see the next cell!\n",
    "\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wait, did that just output `False`??\n",
    "\n",
    "If so, the most common reason is that you didn't save your `.env` file after adding the key! Be sure to have saved.\n",
    "\n",
    "Also, make sure the `.env` file is named precisely `.env` and is in the project root directory (`agents`)\n",
    "\n",
    "By the way, your `.env` file should have a stop symbol next to it in Cursor on the left, and that's actually a good thing: that's Cursor saying to you, \"hey, I realize this is a file filled with secret information, and I'm not going to send it to an external AI to suggest changes, because your keys should not be shown to anyone else.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Final reminders</h2>\n",
    "            <span style=\"color:#ff7800;\">1. If you're not confident about Environment Variables or Web Endpoints / APIs, please read Topics 3 and 5 in this <a href=\"../guides/04_technical_foundations.ipynb\">technical foundations guide</a>.<br/>\n",
    "            2. If you want to use AIs other than OpenAI, like Gemini, DeepSeek or Ollama (free), please see the first section in this <a href=\"../guides/09_ai_apis_and_ollama.ipynb\">AI APIs guide</a>.<br/>\n",
    "            3. If you ever get a Name Error in Python, you can always fix it immediately; see the last section of this <a href=\"../guides/06_python_foundations.ipynb\">Python Foundations guide</a> and follow both tutorials and exercises.<br/>\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key exists and begins sk-proj-\n"
     ]
    }
   ],
   "source": [
    "# Check the key - if you're not using OpenAI, check whichever key you're using! Ollama doesn't need a key.\n",
    "\n",
    "import os\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set - please head to the troubleshooting guide in the setup folder\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now - the all important import statement\n",
    "# If you get an import error - head over to troubleshooting in the Setup folder\n",
    "\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now we'll create an instance of the OpenAI class\n",
    "# If you're not sure what it means to create an instance of a class - head over to the guides folder (guide 6)!\n",
    "# If you get a NameError - head over to the guides folder (guide 6)to learn about NameErrors - always instantly fixable\n",
    "# If you're not using OpenAI, you just need to slightly modify this - precise instructions are in the AI APIs guide (guide 9)\n",
    "\n",
    "openai = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of messages in the familiar OpenAI format\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"What is 2+2?\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 + 2 = 4\n"
     ]
    }
   ],
   "source": [
    "# And now call it! Any problems, head to the troubleshooting guide\n",
    "# This uses GPT 4.1 nano, the incredibly cheap model\n",
    "# The APIs guide (guide 9) has exact instructions for using even cheaper or free alternatives to OpenAI\n",
    "# If you get a NameError, head to the guides folder (guide 6) to learn about NameErrors - always instantly fixable\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-nano\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now - let's ask for a question:\n",
    "\n",
    "question = \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"\n",
    "messages = [{\"role\": \"user\", \"content\": question}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?\n"
     ]
    }
   ],
   "source": [
    "# ask it - this uses GPT 4.1 mini, still cheap but more powerful than nano\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "question = response.choices[0].message.content\n",
    "\n",
    "print(question)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# form a new messages list\n",
    "messages = [{\"role\": \"user\", \"content\": question}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's denote the cost of the ball as \\( x \\) dollars.\n",
      "\n",
      "According to the problem:\n",
      "- The bat costs \\( x + 1.00 \\) dollars.\n",
      "- The total cost is \\( 1.10 \\) dollars.\n",
      "\n",
      "So, we can write the equation:\n",
      "\\[\n",
      "x + (x + 1.00) = 1.10\n",
      "\\]\n",
      "\n",
      "Combine like terms:\n",
      "\\[\n",
      "2x + 1.00 = 1.10\n",
      "\\]\n",
      "\n",
      "Subtract 1.00 from both sides:\n",
      "\\[\n",
      "2x = 0.10\n",
      "\\]\n",
      "\n",
      "Divide both sides by 2:\n",
      "\\[\n",
      "x = 0.05\n",
      "\\]\n",
      "\n",
      "**Answer:** The ball costs **5 cents** (\\$0.05).\n"
     ]
    }
   ],
   "source": [
    "# Ask it again\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "answer = response.choices[0].message.content\n",
    "print(answer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Let's denote the cost of the ball as \\( x \\) dollars.\n",
       "\n",
       "According to the problem:\n",
       "- The bat costs \\( x + 1.00 \\) dollars.\n",
       "- The total cost is \\( 1.10 \\) dollars.\n",
       "\n",
       "So, we can write the equation:\n",
       "\\[\n",
       "x + (x + 1.00) = 1.10\n",
       "\\]\n",
       "\n",
       "Combine like terms:\n",
       "\\[\n",
       "2x + 1.00 = 1.10\n",
       "\\]\n",
       "\n",
       "Subtract 1.00 from both sides:\n",
       "\\[\n",
       "2x = 0.10\n",
       "\\]\n",
       "\n",
       "Divide both sides by 2:\n",
       "\\[\n",
       "x = 0.05\n",
       "\\]\n",
       "\n",
       "**Answer:** The ball costs **5 cents** (\\$0.05)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "display(Markdown(answer))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Congratulations!\n",
    "\n",
    "That was a small, simple step in the direction of Agentic AI, with your new environment!\n",
    "\n",
    "Next time things get more interesting..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
    "            <span style=\"color:#ff7800;\">Now try this commercial application:<br/>\n",
    "            First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.<br/>\n",
    "            Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.<br/>\n",
    "            Finally have 3 third LLM call propose the Agentic AI solution. <br/>\n",
    "            We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One promising business idea for an Agentic AI opportunity is **an Automated Small Business Growth Strategist and Executioner**.\n",
      "\n",
      "### Concept:\n",
      "Develop an AI agent that not only advises small businesses on growth strategies but also autonomously executes key tasks across marketing, sales, customer engagement, and operations without requiring constant human intervention.\n",
      "\n",
      "### Why this is worth exploring:\n",
      "- **Market demand:** Small and medium-sized businesses (SMBs) often lack the budget to hire full-time strategists or multiple specialists.\n",
      "- **Agentic AI fit:** The AI can independently analyze business data, create tailored growth plans, run marketing campaigns, optimize pricing, handle customer inquiries, and adjust tactics in real time.\n",
      "- **Scalability:** Once trained, the AI can serve many clients simultaneously.\n",
      "- **Value proposition:** Helps SMBs accelerate growth, reduce overhead, and compete with larger companies.\n",
      "\n",
      "### Key features/functionalities:\n",
      "- Data ingestion from sales, website analytics, customer feedback\n",
      "- Market research and competitor analysis\n",
      "- Automated ad creation and campaign management\n",
      "- Dynamic pricing and inventory suggestions\n",
      "- Personalized email and SMS outreach\n",
      "- Chatbot-based customer support and lead qualification\n",
      "- Performance tracking and incremental strategy refinement\n",
      "\n",
      "### Challenges to address:\n",
      "- Ensuring the AI’s actions align with each business's unique brand and ethics\n",
      "- Balancing autonomy with user controls and transparency\n",
      "- Integrating with various platforms and tools SMBs use\n",
      "\n",
      "Building such an Agentic AI could transform how SMBs operate by providing accessible, actionable, and continuously optimized growth support.\n"
     ]
    }
   ],
   "source": [
    "# First create the messages:\n",
    "\n",
    "messages_gpt = [{\"role\": \"user\", \"content\": \"pick a business idea that might be worth exploring for an Agentic AI opportunity\"}]\n",
    "\n",
    "# Then make the first call:\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages_gpt\n",
    ")\n",
    "\n",
    "# Then read the business idea:\n",
    "\n",
    "business_idea_gpt = response.choices[0].message.content\n",
    "\n",
    "print(business_idea_gpt)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A significant pain point for the **Automated Small Business Growth Strategist and Executioner** lies in **building and maintaining the trust of small business owners in an AI system that autonomously executes critical growth tasks**. \n",
      "\n",
      "Small business owners often have deep emotional investments and unique visions for their businesses, and handing over substantial control—such as marketing spend, pricing, or customer interactions—to an AI may create anxiety and resistance. They may fear loss of control, potential misalignment with their brand voice or values, or unintended consequences from automated decisions. Overcoming this trust barrier requires transparent AI decision-making, easy-to-understand controls, and reliable safeguards to ensure the AI’s actions feel safe, predictable, and aligned with business goals. Failure to address this pain point could lead to reluctance in adopting the solution, regardless of its features and potential benefits.\n"
     ]
    }
   ],
   "source": [
    "# And repeat! In the next message, include the business idea within the message\n",
    "\n",
    "messages_gpt.append({\"role\": \"assistant\", \"content\": business_idea_gpt})\n",
    "\n",
    "messages_gpt = [{\"role\": \"user\", \"content\": \"Present a pain point for the business idea: \" + business_idea_gpt}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages_gpt\n",
    ")\n",
    "\n",
    "pain_point_gpt = response.choices[0].message.content\n",
    "\n",
    "print(pain_point_gpt)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Certainly! Here is a detailed **Agentic AI solution** designed to address the trust challenges faced by small business owners when entrusting an AI to autonomously execute critical growth tasks:\n",
      "\n",
      "---\n",
      "\n",
      "### Agentic AI Solution: TrustBuilder AI for Small Business Growth\n",
      "\n",
      "**Overview:**  \n",
      "TrustBuilder AI is an autonomous growth strategist and executor specifically designed with transparency, control, and alignment features that directly address the trust concerns of small business owners. It acts as a collaborative partner rather than an opaque tool, ensuring business owners feel ownership, safety, and confidence in every action the AI takes.\n",
      "\n",
      "---\n",
      "\n",
      "### Key Features & Architectural Approaches\n",
      "\n",
      "#### 1. **Explainable & Transparent Decision-Making**\n",
      "- **Real-Time Natural Language Rationales:**  \n",
      "  For each growth action or recommendation (e.g., adjusting marketing spend, changing pricing, launching a campaign), TrustBuilder AI generates a clear, concise explanation in plain language. Example:  \n",
      "  *“I recommend increasing Facebook ad spend by 15% this month because competitor analysis shows a 20% higher conversion rate there, aligning with your business goal to boost local customer engagement.”*\n",
      "\n",
      "- **Decision Path Visualization Dashboard:**  \n",
      "  A visual, interactive flowchart showing how inputs (market data, previous campaign results, customer feedback) led to each decision. This transparency reduces anxiety of “black-box” decisions.\n",
      "\n",
      "---\n",
      "\n",
      "#### 2. **Configurable Control Layers (“Adjustable Autonomy”)**\n",
      "- **Modular Autonomy Settings:**  \n",
      "  Owners can customize autonomy levels per task:\n",
      "  - *Full Automation* for routine executions (e.g., scheduling social posts, reporting)  \n",
      "  - *Human-in-the-Loop* prompts for critical changes (e.g., pricing adjustments) before execution  \n",
      "  - *Recommendation Mode* with zero direct execution—only suggestions for owner approval\n",
      "\n",
      "- **“Trusted Bounds” Constraints:**  \n",
      "  Owners set explicit boundaries such as maximum ad budget changes, pricing floors/ceilings, tone/style guidelines for communication, and risk tolerance levels. The AI raises alerts if proposed changes approach these limits.\n",
      "\n",
      "---\n",
      "\n",
      "#### 3. **Value & Brand Alignment Assurance**\n",
      "- **Values Embedding Module:**  \n",
      "  Upon setup, the owner inputs key business values, brand voice characteristics, and unique selling propositions. TrustBuilder AI incorporates these into a brand profile that guides all automated decisions, ensuring alignment in actions and messaging.\n",
      "\n",
      "- **Periodic Alignment Checks:**  \n",
      "  The AI periodically reviews accumulated outputs and strategy to validate consistency with the brand values. Any deviation triggers a “flag for review” notification.\n",
      "\n",
      "---\n",
      "\n",
      "#### 4. **Robust Safeguards and Fail-Safes**\n",
      "- **Simulation Mode:**  \n",
      "  Before executing any major change autonomously, the AI runs a “what-if” simulation showing projected impacts and potential risks, enabling the owner to approve or tweak the plan.\n",
      "\n",
      "- **Rollback Capability:**  \n",
      "  Any autonomous action can be reversed seamlessly within a defined time window, minimizing fear of irreversible consequences.\n",
      "\n",
      "- **Continuous Monitoring & Anomaly Detection:**  \n",
      "  The system monitors live data post-implementation to detect unexpected negative trends (e.g., sudden drop in customer engagement), triggering automated pause or owner alerts.\n",
      "\n",
      "---\n",
      "\n",
      "#### 5. **Engagement & Education Features**\n",
      "- **Guided Onboarding & Interactive Tutorials:**  \n",
      "  Personalized onboarding that educates owners on how the AI works, benefits, controls, and how to interpret recommendations.\n",
      "\n",
      "- **Progress & Impact Reports:**  \n",
      "  Regular, accessible reports communicated in non-technical language outlining what the AI has done, why, and with what results—helping build ongoing trust through demonstrated value.\n",
      "\n",
      "---\n",
      "\n",
      "### Example User Journey\n",
      "\n",
      "1. **Initial Setup:** The business owner is guided through defining business goals, values, and acceptable autonomy boundaries.\n",
      "2. **Ongoing Execution:** The AI autonomously manages marketing spend within trusted bounds, explaining its moves and requesting approval for pricing changes.\n",
      "3. **Monthly Review:** Owner receives a report with transparent rationales, impact metrics, and can adjust autonomy levels or constraints anytime.\n",
      "4. **Adaptation:** Over time, the AI learns the owner’s preferences and tightens brand alignment, further reducing anxiety and increasing trust.\n",
      "\n",
      "---\n",
      "\n",
      "### Summary  \n",
      "**TrustBuilder AI** empowers small business owners by combining autonomous execution with transparency, configurable control, brand alignment, and fail-safe mechanisms. This agentic AI solution transforms anxiety and resistance into collaboration and confidence, accelerating adoption and maximizing sustained business growth outcomes.\n",
      "\n",
      "---\n",
      "\n",
      "If you want, I can also provide a technical architecture diagram or a prototype feature list for implementation. Would that be helpful?\n"
     ]
    }
   ],
   "source": [
    "messages_gpt.append({\"role\": \"assistant\", \"content\": pain_point_gpt})\n",
    "\n",
    "messages_gpt = [{\"role\": \"user\", \"content\": \"Present an Agentic AI solution for the pain point: \" + pain_point_gpt}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages_gpt\n",
    ")\n",
    "\n",
    "solution_gpt = response.choices[0].message.content\n",
    "\n",
    "print(solution_gpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One promising business idea for an Agentic AI opportunity is **an Automated Small Business Growth Strategist and Executioner**.\n",
      "\n",
      "### Concept:\n",
      "Develop an AI agent that not only advises small businesses on growth strategies but also autonomously executes key tasks across marketing, sales, customer engagement, and operations without requiring constant human intervention.\n",
      "\n",
      "### Why this is worth exploring:\n",
      "- **Market demand:** Small and medium-sized businesses (SMBs) often lack the budget to hire full-time strategists or multiple specialists.\n",
      "- **Agentic AI fit:** The AI can independently analyze business data, create tailored growth plans, run marketing campaigns, optimize pricing, handle customer inquiries, and adjust tactics in real time.\n",
      "- **Scalability:** Once trained, the AI can serve many clients simultaneously.\n",
      "- **Value proposition:** Helps SMBs accelerate growth, reduce overhead, and compete with larger companies.\n",
      "\n",
      "### Key features/functionalities:\n",
      "- Data ingestion from sales, website analytics, customer feedback\n",
      "- Market research and competitor analysis\n",
      "- Automated ad creation and campaign management\n",
      "- Dynamic pricing and inventory suggestions\n",
      "- Personalized email and SMS outreach\n",
      "- Chatbot-based customer support and lead qualification\n",
      "- Performance tracking and incremental strategy refinement\n",
      "\n",
      "### Challenges to address:\n",
      "- Ensuring the AI’s actions align with each business's unique brand and ethics\n",
      "- Balancing autonomy with user controls and transparency\n",
      "- Integrating with various platforms and tools SMBs use\n",
      "\n",
      "Building such an Agentic AI could transform how SMBs operate by providing accessible, actionable, and continuously optimized growth support.\n",
      "A significant pain point for the **Automated Small Business Growth Strategist and Executioner** lies in **building and maintaining the trust of small business owners in an AI system that autonomously executes critical growth tasks**. \n",
      "\n",
      "Small business owners often have deep emotional investments and unique visions for their businesses, and handing over substantial control—such as marketing spend, pricing, or customer interactions—to an AI may create anxiety and resistance. They may fear loss of control, potential misalignment with their brand voice or values, or unintended consequences from automated decisions. Overcoming this trust barrier requires transparent AI decision-making, easy-to-understand controls, and reliable safeguards to ensure the AI’s actions feel safe, predictable, and aligned with business goals. Failure to address this pain point could lead to reluctance in adopting the solution, regardless of its features and potential benefits.\n",
      "Certainly! Here is a detailed **Agentic AI solution** designed to address the trust challenges faced by small business owners when entrusting an AI to autonomously execute critical growth tasks:\n",
      "\n",
      "---\n",
      "\n",
      "### Agentic AI Solution: TrustBuilder AI for Small Business Growth\n",
      "\n",
      "**Overview:**  \n",
      "TrustBuilder AI is an autonomous growth strategist and executor specifically designed with transparency, control, and alignment features that directly address the trust concerns of small business owners. It acts as a collaborative partner rather than an opaque tool, ensuring business owners feel ownership, safety, and confidence in every action the AI takes.\n",
      "\n",
      "---\n",
      "\n",
      "### Key Features & Architectural Approaches\n",
      "\n",
      "#### 1. **Explainable & Transparent Decision-Making**\n",
      "- **Real-Time Natural Language Rationales:**  \n",
      "  For each growth action or recommendation (e.g., adjusting marketing spend, changing pricing, launching a campaign), TrustBuilder AI generates a clear, concise explanation in plain language. Example:  \n",
      "  *“I recommend increasing Facebook ad spend by 15% this month because competitor analysis shows a 20% higher conversion rate there, aligning with your business goal to boost local customer engagement.”*\n",
      "\n",
      "- **Decision Path Visualization Dashboard:**  \n",
      "  A visual, interactive flowchart showing how inputs (market data, previous campaign results, customer feedback) led to each decision. This transparency reduces anxiety of “black-box” decisions.\n",
      "\n",
      "---\n",
      "\n",
      "#### 2. **Configurable Control Layers (“Adjustable Autonomy”)**\n",
      "- **Modular Autonomy Settings:**  \n",
      "  Owners can customize autonomy levels per task:\n",
      "  - *Full Automation* for routine executions (e.g., scheduling social posts, reporting)  \n",
      "  - *Human-in-the-Loop* prompts for critical changes (e.g., pricing adjustments) before execution  \n",
      "  - *Recommendation Mode* with zero direct execution—only suggestions for owner approval\n",
      "\n",
      "- **“Trusted Bounds” Constraints:**  \n",
      "  Owners set explicit boundaries such as maximum ad budget changes, pricing floors/ceilings, tone/style guidelines for communication, and risk tolerance levels. The AI raises alerts if proposed changes approach these limits.\n",
      "\n",
      "---\n",
      "\n",
      "#### 3. **Value & Brand Alignment Assurance**\n",
      "- **Values Embedding Module:**  \n",
      "  Upon setup, the owner inputs key business values, brand voice characteristics, and unique selling propositions. TrustBuilder AI incorporates these into a brand profile that guides all automated decisions, ensuring alignment in actions and messaging.\n",
      "\n",
      "- **Periodic Alignment Checks:**  \n",
      "  The AI periodically reviews accumulated outputs and strategy to validate consistency with the brand values. Any deviation triggers a “flag for review” notification.\n",
      "\n",
      "---\n",
      "\n",
      "#### 4. **Robust Safeguards and Fail-Safes**\n",
      "- **Simulation Mode:**  \n",
      "  Before executing any major change autonomously, the AI runs a “what-if” simulation showing projected impacts and potential risks, enabling the owner to approve or tweak the plan.\n",
      "\n",
      "- **Rollback Capability:**  \n",
      "  Any autonomous action can be reversed seamlessly within a defined time window, minimizing fear of irreversible consequences.\n",
      "\n",
      "- **Continuous Monitoring & Anomaly Detection:**  \n",
      "  The system monitors live data post-implementation to detect unexpected negative trends (e.g., sudden drop in customer engagement), triggering automated pause or owner alerts.\n",
      "\n",
      "---\n",
      "\n",
      "#### 5. **Engagement & Education Features**\n",
      "- **Guided Onboarding & Interactive Tutorials:**  \n",
      "  Personalized onboarding that educates owners on how the AI works, benefits, controls, and how to interpret recommendations.\n",
      "\n",
      "- **Progress & Impact Reports:**  \n",
      "  Regular, accessible reports communicated in non-technical language outlining what the AI has done, why, and with what results—helping build ongoing trust through demonstrated value.\n",
      "\n",
      "---\n",
      "\n",
      "### Example User Journey\n",
      "\n",
      "1. **Initial Setup:** The business owner is guided through defining business goals, values, and acceptable autonomy boundaries.\n",
      "2. **Ongoing Execution:** The AI autonomously manages marketing spend within trusted bounds, explaining its moves and requesting approval for pricing changes.\n",
      "3. **Monthly Review:** Owner receives a report with transparent rationales, impact metrics, and can adjust autonomy levels or constraints anytime.\n",
      "4. **Adaptation:** Over time, the AI learns the owner’s preferences and tightens brand alignment, further reducing anxiety and increasing trust.\n",
      "\n",
      "---\n",
      "\n",
      "### Summary  \n",
      "**TrustBuilder AI** empowers small business owners by combining autonomous execution with transparency, configurable control, brand alignment, and fail-safe mechanisms. This agentic AI solution transforms anxiety and resistance into collaboration and confidence, accelerating adoption and maximizing sustained business growth outcomes.\n",
      "\n",
      "---\n",
      "\n",
      "If you want, I can also provide a technical architecture diagram or a prototype feature list for implementation. Would that be helpful?\n"
     ]
    }
   ],
   "source": [
    "print(business_idea_gpt)\n",
    "print(pain_point_gpt)\n",
    "print(solution_gpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anthropic import Anthropic\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "anthropic = Anthropic(api_key=os.getenv(\"ANTHROPIC_API_KEY\"))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's a business idea worth exploring in the Agentic AI space:\n",
      "\n",
      "AI-Powered Personal Research Assistant\n",
      "\n",
      "Core Concept:\n",
      "An AI agent that helps professionals, academics, and knowledge workers conduct comprehensive research by:\n",
      "\n",
      "1. Understanding complex research queries\n",
      "2. Gathering information from multiple sources\n",
      "3. Synthesizing findings\n",
      "4. Identifying patterns and connections\n",
      "5. Generating actionable insights and summaries\n",
      "\n",
      "Key Features:\n",
      "- Autonomous information gathering across academic databases, news sources, and public documents\n",
      "- Source verification and credibility assessment\n",
      "- Custom knowledge domain adaptation\n",
      "- Citation management and formatting\n",
      "- Interactive follow-up questions and clarifications\n",
      "- Integration with popular research and writing tools\n",
      "\n",
      "Target Market:\n",
      "- Academic researchers\n",
      "- Business analysts\n",
      "- Journalists\n",
      "- Legal professionals\n",
      "- Market researchers\n",
      "- Policy makers\n",
      "\n",
      "Value Proposition:\n",
      "- Significant time savings in research processes\n",
      "- More comprehensive coverage of available information\n",
      "- Reduced risk of missing important sources or connections\n",
      "- Better organized and structured research outputs\n",
      "- Scalable knowledge gathering and synthesis\n",
      "\n",
      "This idea leverages the emerging capabilities of agentic AI while addressing a clear market need for more efficient and thorough research processes.\n",
      "\n",
      "Would you like me to elaborate on any aspect of this business idea?\n"
     ]
    }
   ],
   "source": [
    "messages_anthropic = [{\"role\": \"user\", \"content\": \"pick a business idea that might be worth exploring for an Agentic AI opportunity\"}]\n",
    "\n",
    "response = anthropic.messages.create(\n",
    "    model=\"claude-3-5-sonnet-latest\",\n",
    "    max_tokens=1000,\n",
    "    messages=messages_anthropic)\n",
    "\n",
    "business_idea_anthropic = response.content[0].text\n",
    "\n",
    "\n",
    "messages_anthropic.append({\"role\": \"assistant\", \"content\": business_idea_anthropic})\n",
    "\n",
    "print(business_idea_anthropic)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's a significant pain point for this AI-Powered Personal Research Assistant business idea:\n",
      "\n",
      "Data Access and Licensing Challenges\n",
      "\n",
      "The ability to access and legally use content from premium academic databases, professional journals, and paywalled sources presents a major hurdle. Many valuable research sources:\n",
      "\n",
      "- Require expensive institutional subscriptions\n",
      "- Have strict licensing terms that may prohibit AI-powered scraping\n",
      "- Maintain complex API access requirements\n",
      "- Charge significant fees for programmatic access\n",
      "- Have varying terms of use across different regions\n",
      "\n",
      "This creates several problems:\n",
      "1. High operating costs to maintain necessary database subscriptions\n",
      "2. Legal complexity in ensuring compliance with multiple licensing agreements\n",
      "3. Potential gaps in research coverage due to inaccessible sources\n",
      "4. Need for complex negotiations with multiple content providers\n",
      "5. Risk of inadvertently violating terms of service\n",
      "\n",
      "This pain point could significantly impact both the service's comprehensiveness and its pricing model, potentially limiting its value proposition for users who need access to specialized or premium content sources.\n"
     ]
    }
   ],
   "source": [
    "messages_anthropic = [{\"role\": \"user\", \"content\": \"Present a pain point for the business idea: \" + business_idea_anthropic}]\n",
    "\n",
    "response = anthropic.messages.create(\n",
    "    model=\"claude-3-5-sonnet-latest\",\n",
    "    max_tokens=1000,\n",
    "    messages=messages_anthropic)\n",
    "\n",
    "pain_point_anthropic = response.content[0].text\n",
    "\n",
    "messages_anthropic.append({\"role\": \"assistant\", \"content\": pain_point_anthropic})\n",
    "\n",
    "print(pain_point_anthropic)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'll provide a solution by acting as an AI Strategic Solutions Architect. Let me break this down into a comprehensive, actionable solution.\n",
      "\n",
      "PROPOSED SOLUTION: Tiered Access Partnership Network (TAPN)\n",
      "\n",
      "1. CORE INFRASTRUCTURE\n",
      "- Develop a modular API integration framework that can flexibly connect to different content providers\n",
      "- Create a content source management system that tracks licensing terms and usage rights\n",
      "- Implement real-time compliance monitoring tools\n",
      "\n",
      "2. PARTNERSHIP STRATEGY\n",
      "- Establish strategic partnerships with major academic institutions for shared access rights\n",
      "- Create a consortium model where multiple research organizations pool resources\n",
      "- Negotiate bulk licensing deals with content providers offering volume discounts\n",
      "\n",
      "3. OPERATIONAL MODEL\n",
      "\n",
      "Tier 1: Open Access\n",
      "- Utilize freely available academic repositories (arXiv, PubMed Central)\n",
      "- Partner with open science initiatives\n",
      "- Integrate public domain research databases\n",
      "\n",
      "Tier 2: Institution-Linked Access\n",
      "- Allow users to link their institutional credentials\n",
      "- Create passport system for verified academic users\n",
      "- Implement institutional SSO (Single Sign-On) integration\n",
      "\n",
      "Tier 3: Premium Partnership Access\n",
      "- Direct licensing agreements with publishers\n",
      "- Custom API access arrangements\n",
      "- Specialized content packages\n",
      "\n",
      "4. TECHNICAL IMPLEMENTATION\n",
      "\n",
      "Content Access Layer:\n",
      "```python\n",
      "class ContentAccessManager:\n",
      "    def __init__(self):\n",
      "        self.access_levels = {\n",
      "            'open': OpenAccessHandler(),\n",
      "            'institutional': InstitutionalAccessHandler(),\n",
      "            'premium': PremiumAccessHandler()\n",
      "        }\n",
      "        \n",
      "    def retrieve_content(self, source, user_credentials):\n",
      "        access_level = self.determine_access_level(user_credentials)\n",
      "        handler = self.access_levels[access_level]\n",
      "        return handler.fetch_content(source)\n",
      "```\n",
      "\n",
      "5. RISK MITIGATION\n",
      "- Implement real-time usage tracking\n",
      "- Develop automated compliance checking\n",
      "- Create audit trails for content access\n",
      "- Regular license term reviews\n",
      "\n",
      "6. REVENUE MODEL ALIGNMENT\n",
      "- Usage-based pricing for premium content\n",
      "- Institution-based subscription models\n",
      "- Pay-per-access options for specialized content\n",
      "\n",
      "7. SCALABILITY APPROACH\n",
      "- Start with core open access sources\n",
      "- Gradually expand partnership network\n",
      "- Add premium sources based on user demand\n",
      "\n",
      "8. MARKET POSITIONING\n",
      "\"Research Without Boundaries - Compliant Access to Global Knowledge\"\n",
      "\n",
      "EXECUTION TIMELINE:\n",
      "\n",
      "Phase 1 (Months 1-3):\n",
      "- Implement open access integration\n",
      "- Develop basic compliance framework\n",
      "- Launch institutional partnership program\n",
      "\n",
      "Phase 2 (Months 4-6):\n",
      "- Roll out premium partnerships\n",
      "- Expand content provider network\n",
      "- Enhance compliance monitoring\n",
      "\n",
      "Phase 3 (Months 7-12):\n",
      "- Scale partnership network\n",
      "- Optimize access protocols\n",
      "- Refine pricing models\n",
      "\n",
      "SUCCESS METRICS:\n",
      "1. Content coverage percentage\n",
      "2. Licensing compliance rate\n",
      "3. User access satisfaction\n",
      "4. Partnership network growth\n",
      "5. Cost per accessed document\n",
      "\n",
      "CONTINUOUS IMPROVEMENT:\n",
      "- Regular partnership reviews\n",
      "- Compliance protocol updates\n",
      "- User feedback integration\n",
      "- Technology stack optimization\n",
      "\n",
      "This solution transforms the challenge into a structured opportunity while ensuring:\n",
      "- Legal compliance\n",
      "- Scalable access\n",
      "- Cost-effective operation\n",
      "- User satisfaction\n",
      "- Long-term sustainability\n",
      "\n",
      "Would you like me to elaborate on any specific aspect of this solution?\n"
     ]
    }
   ],
   "source": [
    "messages_anthropic = [{\"role\": \"user\", \"content\": \"Present an agentic Ai solution for the following pain point: \" + pain_point_anthropic}]\n",
    "\n",
    "response = anthropic.messages.create(\n",
    "    model=\"claude-3-5-sonnet-latest\",\n",
    "    max_tokens=1000,\n",
    "    messages=messages_anthropic\n",
    ")\n",
    "\n",
    "solution_anthropic = response.content[0].text\n",
    "\n",
    "messages_anthropic.append({\"role\": \"assistant\", \"content\": solution_anthropic})\n",
    "\n",
    "print(solution_anthropic)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's an agentic AI solution to address the trust-building challenge for the Automated Small Business Growth Strategist and Executioner:\n",
      "\n",
      "**Solution: The \"Trust Bridge\" AI Companion**\n",
      "\n",
      "**Core Function:**\n",
      "An AI system that builds trust through progressive autonomy and collaborative learning, functioning as both an executor and educator.\n",
      "\n",
      "**Key Components:**\n",
      "\n",
      "1. **Transparent Decision Dashboard**\n",
      "- Real-time visualization of AI decision-making processes\n",
      "- Clear cause-and-effect explanations for each action\n",
      "- Preview of potential outcomes before execution\n",
      "- Historical performance tracking\n",
      "\n",
      "2. **Progressive Autonomy System**\n",
      "```python\n",
      "autonomy_levels = {\n",
      "    'Level 1: Shadow Mode': 'AI observes and suggests only',\n",
      "    'Level 2: Supervised Execution': 'AI acts with approval',\n",
      "    'Level 3: Bounded Autonomy': 'AI acts within set parameters',\n",
      "    'Level 4: Full Autonomy': 'AI operates independently'\n",
      "}\n",
      "```\n",
      "\n",
      "3. **Value Alignment Protocol**\n",
      "- Initial business values and goals assessment\n",
      "- Regular alignment checks\n",
      "- Automated brand voice calibration\n",
      "- Cultural sensitivity monitoring\n",
      "\n",
      "4. **Emergency Override System**\n",
      "- Instant pause/stop functionality\n",
      "- Quick rollback capabilities\n",
      "- 24/7 human support backup\n",
      "- Automated risk detection\n",
      "\n",
      "**Trust-Building Workflow:**\n",
      "\n",
      "1. **Onboarding Phase**\n",
      "```python\n",
      "def onboarding():\n",
      "    collect_business_values()\n",
      "    establish_baseline_metrics()\n",
      "    set_initial_constraints()\n",
      "    create_safety_parameters()\n",
      "```\n",
      "\n",
      "2. **Learning Phase**\n",
      "```python\n",
      "def learning_cycle():\n",
      "    while trust_score < threshold:\n",
      "        shadow_mode_operation()\n",
      "        collect_owner_feedback()\n",
      "        adjust_parameters()\n",
      "        demonstrate_value()\n",
      "```\n",
      "\n",
      "3. **Execution Phase**\n",
      "```python\n",
      "def execute_with_trust():\n",
      "    verify_alignment()\n",
      "    preview_actions()\n",
      "    if approved:\n",
      "        implement_changes()\n",
      "    track_results()\n",
      "    provide_explanations()\n",
      "```\n",
      "\n",
      "**Trust-Building Features:**\n",
      "\n",
      "1. **Collaborative Control Panel**\n",
      "- Customizable control settings\n",
      "- Easy-to-set boundaries\n",
      "- Visual performance metrics\n",
      "- Action history logs\n",
      "\n",
      "2. **Predictive Impact Analysis**\n",
      "- Revenue forecasting\n",
      "- Risk assessment\n",
      "- Resource allocation preview\n",
      "- Customer sentiment prediction\n",
      "\n",
      "3. **Communication Protocol**\n",
      "```python\n",
      "def communication_system():\n",
      "    daily_summary_reports()\n",
      "    alert_on_significant_changes()\n",
      "    provide_context_for_decisions()\n",
      "    suggest_optimization_opportunities()\n",
      "```\n",
      "\n",
      "4. **Safety Mechanisms**\n",
      "- Budget limits\n",
      "- Brand voice guidelines\n",
      "- Customer interaction parameters\n",
      "- Performance thresholds\n",
      "\n",
      "**Implementation Strategy:**\n",
      "\n",
      "1. **Phase 1: Trust Foundation**\n",
      "- Initial assessment period\n",
      "- Basic automation implementation\n",
      "- Heavy supervision and feedback\n",
      "- Trust metric establishment\n",
      "\n",
      "2. **Phase 2: Capability Expansion**\n",
      "- Gradual increase in autonomy\n",
      "- Regular performance reviews\n",
      "- Adjustment of parameters\n",
      "- Trust reinforcement\n",
      "\n",
      "3. **Phase 3: Full Integration**\n",
      "- Complete system deployment\n",
      "- Ongoing monitoring\n",
      "- Continuous improvement\n",
      "- Trust maintenance\n",
      "\n",
      "**Success Metrics:**\n",
      "\n",
      "```python\n",
      "trust_metrics = {\n",
      "    'owner_confidence_score': float,\n",
      "    'override_frequency': int,\n",
      "    'positive_outcome_rate': float,\n",
      "    'alignment_score': float,\n",
      "    'response_time': float\n",
      "}\n",
      "```\n",
      "\n",
      "**Expected Outcomes:**\n",
      "\n",
      "1. **Business Owner Benefits**\n",
      "- Increased confidence in AI decisions\n",
      "- Better understanding of AI processes\n",
      "- Maintained sense of control\n",
      "- Improved business results\n",
      "\n",
      "2. **System Benefits**\n",
      "- Higher adoption rates\n",
      "- Reduced override frequency\n",
      "- Better performance through trust\n",
      "- Stronger AI-owner relationships\n",
      "\n",
      "3. **Long-term Impact**\n",
      "- Sustainable business growth\n",
      "- Scalable automation\n",
      "- Improved efficiency\n",
      "- Enhanced decision-making\n",
      "\n",
      "This solution addresses the trust barrier by creating a transparent, collaborative environment where business owners can gradually develop confidence in the AI system while maintaining appropriate control and oversight. The progressive autonomy approach allows for natural trust-building while delivering measurable business benefits.\n"
     ]
    }
   ],
   "source": [
    "messages_anthropic = [{\"role\": \"user\", \"content\": \"Present an agentic Ai solution for the following pain point: \" + pain_point_gpt}]\n",
    "\n",
    "\n",
    "\n",
    "response = anthropic.messages.create(\n",
    "    model=\"claude-3-5-sonnet-latest\",\n",
    "    max_tokens=1000,\n",
    "    messages=messages_anthropic\n",
    ")\n",
    "\n",
    "solution_anthropic_pain_point_gpt = response.content[0].text\n",
    "\n",
    "print(solution_anthropic_pain_point_gpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Certainly! Below is a detailed **agentic AI solution** framework addressing the \"Data Access and Licensing Challenges\" for an AI-Powered Personal Research Assistant service:\n",
      "\n",
      "---\n",
      "\n",
      "## Agentic AI Solution: Intelligent Content Access & Compliance Orchestrator (ICACO)\n",
      "\n",
      "### Overview\n",
      "ICACO is an autonomous AI agent layered inside the research assistant ecosystem, designed to **intelligently manage, negotiate, and optimize compliant access to premium and paywalled content** while minimizing costs and legal risks. It acts as both an operational and strategic agent that automates complex data licensing workflows, content aggregation planning, and compliance auditing.\n",
      "\n",
      "---\n",
      "\n",
      "### Key Functional Components\n",
      "\n",
      "#### 1. **Dynamic Licensing Intelligence Module**\n",
      "- **Function:** Continuously scans and ingests licensing terms, API policies, regional laws, and content usage restrictions from a wide array of sources (including providers’ portals, legal databases, and industry updates).\n",
      "- **AI Role:** Uses NLP and semantic understanding to parse complex licensing documents and generate structured summaries of key terms (e.g., data usage limits, permitted API calls, AI-scraping policies).\n",
      "\n",
      "#### 2. **Automated Licensing Negotiation Agent**\n",
      "- **Function:** Identifies subscription gaps and initiates automated negotiation workflows.\n",
      "- **AI Role:** Utilizes pre-trained negotiation strategies and contextual knowledge about market rates and value propositions to engage with content providers (via emails, portals, or APIs). Proposes volume discounts, custom access packages, consortium memberships, or revenue-sharing models.\n",
      "- **Business Impact:** Reduces human legal/negotiation effort and operational costs, and discovers innovative licensing terms dynamically.\n",
      "\n",
      "#### 3. **Content Source Optimization Engine**\n",
      "- **Function:** Strategically selects and prioritizes content sources to maximize coverage vs cost.\n",
      "- **AI Role:** Balances user queries’ topicality, content provider access costs, licensing risk scores, and regional legal parameters to route queries to accessible sources (open access, licensed, or premium). Leverages a knowledge graph for interlinking topic relevance and source availability.\n",
      "- **Benefit:** Prevents unnecessary expensive access; fills content gaps intelligently.\n",
      "\n",
      "#### 4. **Compliance & Usage Monitoring Agent**\n",
      "- **Function:** Tracks actual content retrieval, usage, and downstream AI utilization.\n",
      "- **AI Role:** Applies anomaly detection to flag potential unauthorized usage against licensed quotas or terms. Generates compliance reports and alerts for renewal negotiations.\n",
      "- **Legal Safeguard:** Minimizes risk of unnoticed TOS violations and potential lawsuit exposure.\n",
      "\n",
      "#### 5. **Federated Access Broker**\n",
      "- **Function:** Connects to institutional accounts, academic consortiums, and cooperative content-sharing networks.\n",
      "- **AI Role:** Facilitates single-sign-on, token exchange, and respects access control policies for users affiliated with external institutions (universities, research labs). Coordinates shared subscription pooling.\n",
      "- **User Benefit:** Expands premium content access without duplicate subscriptions.\n",
      "\n",
      "#### 6. **User-Permissioned Crowdsourced Content Aggregator**\n",
      "- **Function:** With explicit user consent, aggregates additive research content (e.g., user-uploaded or shared documents).\n",
      "- **AI Role:** Validates copyright compliance, integrates crowdsourced content to fill paywalled gaps, and enhances personalization.\n",
      "- **Community Aspect:** Builds a compliant supplementary content layer.\n",
      "\n",
      "---\n",
      "\n",
      "### Workflow Example\n",
      "\n",
      "1. **User Query:** A researcher requests insights on a niche biotech topic.\n",
      "2. **Content Source Optimization:** ICACO evaluates applicable sources — accesses open datasets, licensed journals, AND polls consortium-shared premium content.\n",
      "3. **Automated Licensing Negotiation:** Identifies a provider offering expensive paywalled content; ICACO triggers a negotiation chatbot offering a pay-per-use add-on deal.\n",
      "4. **Compliance Agent:** Ensures the retrieved data usage abides by license terms.\n",
      "5. **Result Aggregation:** Synthesizes findings with permitted data to present comprehensive, legally compliant, and current answers.\n",
      "\n",
      "---\n",
      "\n",
      "### Advantages of ICACO Agentic AI Approach\n",
      "\n",
      "- **Cost Efficiency:** Smart negotiation + source optimization reduce subscription overhead.\n",
      "- **Legal Safety:** Automated compliance monitoring mitigates risk of license violations.\n",
      "- **Comprehensive Coverage:** Dynamic, federated, and crowdsourced content access minimize gaps.\n",
      "- **Scalability:** Agent continuously learns and adapts to changing licensing landscapes and regulations.\n",
      "- **Competitive Differentiation:** Enables offering premium content access with transparent, legally sound pricing models.\n",
      "\n",
      "---\n",
      "\n",
      "### Implementation Considerations\n",
      "\n",
      "- Develop partnerships with legal AI providers for licensing interpretation.\n",
      "- Build robust natural language negotiation bots empowered by dialog management frameworks.\n",
      "- Invest in federated identity and access management systems.\n",
      "- Integrate blockchain or tamper-evident logging for auditable compliance trails.\n",
      "- Ensure GDPR, CCPA, and region-specific legal compliance in data handling.\n",
      "\n",
      "---\n",
      "\n",
      "**In summary**, the ICACO agent acts as a proactive, autonomous legal-licensing strategist and content orchestrator, transforming the AI research assistant’s paywalled data challenge into a manageable, cost-effective, and legally compliant advantage. This solution not only addresses the core pain point but also enhances trust, scalability, and overall value proposition.\n"
     ]
    }
   ],
   "source": [
    "messages_gpt = [{\"role\": \"user\", \"content\": \"Present an agentic Ai solution for the following pain point: \" + pain_point_anthropic}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages_gpt\n",
    ")\n",
    "\n",
    "solution_gpt_pain_point_anthropic = response.choices[0].message.content\n",
    "\n",
    "messages_gpt.append({\"role\": \"assistant\", \"content\": solution_gpt_pain_point_anthropic})\n",
    "\n",
    "\n",
    "print(solution_gpt_pain_point_anthropic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "# Create an OpenAI client that points to your local ollama server\n",
    "client = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\") \n",
    "\n",
    "MODEL = \"llama3.2:latest\"  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Business Idea:**\n",
      "\n",
      "**Company Name:** Augma AI Solutions\n",
      "\n",
      "**Mission Statement:** To apply the power of agentic AI to enhance decision-making, strategic planning, and outcomes in various industries, ensuring agility, resilience, and long-term success.\n",
      "\n",
      "**Description:**\n",
      "\n",
      "Augma AI Solutions will specialize in developing and implementing advanced agentic AI solutions for organizations. Agentic AI refers to a type of machine learning that incorporates elements of artificial general intelligence (AGI) with the adaptability and self-learning capabilities of autonomous systems.\n",
      "\n",
      "Our primary mission is to provide clients with cutting-edge, adaptive decision-making tools that can navigate complex environments, anticipate emerging opportunities or threats, and evolve over time to optimize outcomes. By harnessing the power of agentic AI, we aim to unlock business potential in areas such as:\n",
      "\n",
      "**Key Markets:**\n",
      "\n",
      "1. **Strategic Planning:** Helping organizations create agile plans that adapt quickly to shifting market conditions.\n",
      "2. **Risk Management:** Developing AI-powered risk assessment tools that predict and prepare for potential threats.\n",
      "3. **Competitive Intelligence:** Providing actionable insights and forecasting capabilities to inform business strategy.\n",
      "4. **Operational Excellence:** Enhancing supply chain management, logistics optimization, and manufacturing processes using agentic AI.\n",
      "\n",
      "**Key Services:**\n",
      "\n",
      "1. **Strategic Assessment:** Conducting thorough market research and risk analysis followed by customized agentic AI solution development.\n",
      "2. **Adaptive Planning Software:** Designing software platforms that incorporate real-time feedback mechanisms to optimize decision-making.\n",
      "3. **Competitive Intelligence Tools:** Developing AI-powered tools for data collection, analysis, and dissemination of actionable insights.\n",
      "4. **AI Training & Consulting:** Offering training and expert consulting services on the development, deployment, and operation of agentic AI solutions.\n",
      "\n",
      "**Revenue Streams:**\n",
      "\n",
      "1. **Software Licensing:** Leverage our developed technology to generate recurring revenue streams from software licensing agreements with clients across multiple industries.\n",
      "2. **Strategic Partnerships:** Collaborate with major corporations, consulting firms, and research institutions to provide exclusive access to Augma AI Solutions' expertise.\n",
      "3. **Professional Services:** Revenue will be generated from engagement services including strategic planning workshops, competitive intelligence engagements, training & coaching.\n",
      "\n",
      "**Competitive Advantage:**\n",
      "\n",
      "* In-depth knowledge of agentic AI techniques\n",
      "* Proven partnerships with renowned academia organizations for research collaborations\n",
      "* Unique approach to develop custom software to suit distinct business needs\n",
      "* Strong commitment to ongoing innovation in AI capabilities\n",
      "\n",
      "**Management Team:**\n",
      "\n",
      "* CEO (AI strategist and business development expert)\n",
      "* Head of Product Team (product manager, software engineer, & UX/UI designer specializing in AI applications)\n",
      "\n",
      "By addressing the critical need for agility, adaptability, and precision decision-making across various industries, Augma AI Solutions will firmly establish itself as a go-to solution provider for companies seeking to transform their competitive edge.\n",
      "\n",
      "**Projected Milestones:**\n",
      "\n",
      "* First commercial product release within 30 months.\n",
      "* Development of strategic partnerships with three major corporations by end-of-year 3.\n",
      "* Achievement and expansion of additional 100+ client engagements across diverse sectors over the next five years.\n"
     ]
    }
   ],
   "source": [
    "messages_ollama = [\n",
    "    {\"role\": \"user\", \"content\": \"Present a business idea for agentic ai\"}\n",
    "    ]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL,  # or whatever model you have installed\n",
    "    messages=messages_ollama,\n",
    "    # stream=True,\n",
    "    # stream_options={\n",
    "    #     \"include_usage\": True,\n",
    "    #     \"include_response_metadata\": True,\n",
    "    #     \"include_response_metadata\": True,\n",
    "    # }\n",
    ")\n",
    "  \n",
    "business_idea_ollama = response.choices[0].message.content\n",
    "\n",
    "\n",
    "\n",
    "print(business_idea_ollama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's a potential pain point for Augma AI Solutions:\n",
      "\n",
      "**Pain Point:** \"Inadequate decision-making and adaptability are causing significant distress in today's fast-paced, complex business environments. Companies struggle to anticipate emerging opportunities or threats, leading to missed revenue streams, increased risk, and decreased competitiveness.\n",
      "\n",
      "**Root Cause Analysis:**\n",
      "\n",
      "1. **Lack of visibility into market dynamics**: Insufficient access to real-time data, leading to incorrect assumptions and uninformed decisions.\n",
      "2. **Inefficient decision-making processes**: Manual analysis and forecasting methods can't keep pace with changing circumstances, resulting in slow response times and suboptimal outcomes.\n",
      "3.**Insufficient talent and resources**: Limited expertise in AI-powered decision support systems can hinder a company's ability to stay competitive.\n",
      "\n",
      "**Personal Stakeholder Pain Points:**\n",
      "\n",
      "1. **Senior executives struggling to make informed decisions**: Feeling overwhelmed by the complexity of market changes and uncertain about how to prioritize strategic investments.\n",
      "2.**Mid-level managers seeking data-driven insights**: Overwhelmed by the volume and velocity of data, yet unable to extract actionable intelligence from it.\n",
      "3.**Operations teams frustrated with inefficient processes**: Spending too much time on manual tasks that could be automated using AI-powered tools.\n",
      "\n",
      "**Business Impacts:**\n",
      "\n",
      "1. **Revenue loss due to missed opportunities**\n",
      "2. **Increased operational costs due to inefficiencies**\n",
      "3. **Decreased employee morale and engagement resulting from ineffective management processes**\n",
      "\n",
      "**Desired Outcome:** Companies want an agile, adaptive decision-making system that integrates with their existing infrastructure, provides actionable insights, and accelerates time-to-value without a substantial upfront investment.\n",
      "\n",
      "**Augma AI Solutions' Solution:**\n",
      "\n",
      "Address the pain point by providing customizable, agentic AI-powered solutions that enable organizations to navigate complex environments, predict emerging opportunities or threats, and evolve over time to optimize outcomes.\"\n"
     ]
    }
   ],
   "source": [
    "messages_ollama.append({\"role\": \"user\", \"content\": business_idea_ollama})\n",
    "\n",
    "messages_ollama = [{\"role\": \"user\", \"content\": \"Present a pain point for the business idea \" + business_idea_ollama}]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL,\n",
    "    messages=messages_ollama,\n",
    ")\n",
    "\n",
    "pain_point_ollama = response.choices[0].message.content\n",
    "\n",
    "\n",
    "print(pain_point_ollama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's a potential solution for Augma AI Solutions:\n",
      "\n",
      "**Introducing \"Pulse\" - An Agentic AI Decision-Making Platform**\n",
      "\n",
      "Pulse is an adaptive, cloud-based platform designed to empower organizations to make informed decisions in real-time. By leveraging advanced agentic AI capabilities, Pulse provides actionable insights, automates decision-making processes, and integrates seamlessly with existing infrastructure.\n",
      "\n",
      "**Key Features:**\n",
      "\n",
      "1. **Real-Time Market Intelligence**: Pulse aggregates relevant data from multiple sources, providing a unified view of market dynamics and enabling data-driven decision-making.\n",
      "2. **Agentic Decision-Making Engine**: Our unique agentic AI engine enables the platform to recognize patterns, anticipate emerging opportunities or threats, and adjust decision-making processes accordingly.\n",
      "3. **Intelligent Recommendations**: Pulse provides personalized recommendations based on organizational objectives, industry trends, and market conditions, empowering users to make informed decisions quickly.\n",
      "4. **Self-Improving Capabilities**: The agentic AI engine continuously learns from user interactions, refining its decision-making processes and adaptability over time.\n",
      "5. **Integration with Existing Systems**: Pulse seamlessly integrates with existing infrastructure, including CRM, ERP, and other business systems, ensuring minimal disruption to operations.\n",
      "\n",
      "**Benefits:**\n",
      "\n",
      "1. **Enhanced Decision-Making Capabilities**: Organizations can make data-driven decisions in real-time, leveraging actionable insights from Pulse.\n",
      "2. **Increased Agility**: Agentic AI capabilities enable adaptive response to changing market conditions, ensuring the organization remains competitive.\n",
      "3. **Reduced Operational Costs**: Automated decision-making processes and intelligent recommendations minimize manual tasks, freeing up resources for strategic initiatives.\n",
      "4. **Improved Employee Engagement**: Employees have greater confidence in management decisions, enabling increased morale and engagement.\n",
      "5. **Revenue Growth**: By identifying emerging opportunities and mitigating risks, organizations can capitalize on new revenue streams and accelerate growth.\n",
      "\n",
      "**Implementation Options:**\n",
      "\n",
      "1. **Cloud-Based deployment**: Pulse is deployed on our cloud infrastructure, ensuring scalability, security, and flexibility.\n",
      "2. **Managed Services**: Augma AI Solutions provides managed services to ensure seamless integration with existing systems, ongoing support, and expertise in agentic AI capabilities.\n",
      "3. **Customization Packages**: Organizations can opt for a tailored package that meets their specific needs, including the scope of data collection, decision-making protocols, and training programs.\n",
      "\n",
      "By offering Pulse, Augma AI Solutions addresses the pain point by providing a customizable, agentic AI-powered solution that enables organizations to navigate complex environments, predict emerging opportunities or threats, and evolve over time to optimize outcomes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "messages_ollama.append({\"role\": \"assistant\", \"content\": pain_point_ollama})\n",
    "messages_ollama = [\n",
    "    {\"role\": \"user\", \"content\": \"Present an agentic ai solution for the following pain point: \" + pain_point_ollama }\n",
    "    ]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL,  # or whatever model you have installed\n",
    "    messages=messages_ollama,\n",
    "    # stream=True,\n",
    "    # stream_options={\n",
    "    #     \"include_usage\": True,\n",
    "    #     \"include_response_metadata\": True,\n",
    "    #     \"include_response_metadata\": True,\n",
    "    # }\n",
    ")\n",
    "\n",
    "solution_ollama = response.choices[0].message.content\n",
    "\n",
    "print(solution_ollama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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