{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Welcome to the start of your adventure in Agentic AI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Are you ready for action??

\n", " Have you completed all the setup steps in the setup folder?
\n", " Have you read the README? Many common questions are answered here!
\n", " Have you checked out the guides in the guides folder?
\n", " Well in that case, you're ready!!\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

This code is a live resource - keep an eye out for my updates

\n", " 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.

\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", " \n", "
" ] }, { "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": [ "\n", " \n", " \n", " \n", " \n", "
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Final reminders

\n", " 1. If you're not confident about Environment Variables or Web Endpoints / APIs, please read Topics 3 and 5 in this technical foundations guide.
\n", " 2. If you want to use AIs other than OpenAI, like Gemini, DeepSeek or Ollama (free), please see the first section in this AI APIs guide.
\n", " 3. If you ever get a Name Error in Python, you can always fix it immediately; see the last section of this Python Foundations guide and follow both tutorials and exercises.
\n", " \n", "
" ] }, { "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", "# Even for other LLM providers like Gemini, you still use this OpenAI import - see Guide 9 for why\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 equals 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": [ "If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?\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 analyze the problem step-by-step:\n", "\n", "- **Given:** 5 machines take 5 minutes to make 5 widgets.\n", "- **Question:** How long do 100 machines take to make 100 widgets?\n", "\n", "---\n", "\n", "### Step 1: Find the rate of one machine.\n", "\n", "If 5 machines make 5 widgets in 5 minutes, then:\n", "\n", "- Total machine-minutes to make 5 widgets = 5 machines * 5 minutes = 25 machine-minutes.\n", "- So, the time per widget per machine is \\( \\frac{25 \\text{ machine-minutes}}{5 \\text{ widgets}} = 5 \\text{ machine-minutes per widget} \\).\n", "\n", "This means **one widget requires 5 machine-minutes** to be produced.\n", "\n", "---\n", "\n", "### Step 2: Determine production with 100 machines.\n", "\n", "- Each widget requires 5 machine-minutes.\n", "- With 100 machines working in parallel, in one minute, they can do 100 machine-minutes of work.\n", "\n", "---\n", "\n", "### Step 3: Calculate the time to make 100 widgets.\n", "\n", "- Total machine-minutes needed for 100 widgets = \\(100 \\text{ widgets} \\times 5 \\text{ machine-minutes/widget} = 500 \\text{ machine-minutes}\\).\n", "- Since 100 machines can contribute 100 machine-minutes per minute, the time taken \\(t\\) is:\n", "\n", "\\[\n", "t = \\frac{500 \\text{ machine-minutes}}{100 \\text{ machine-minutes/min}} = 5 \\text{ minutes}\n", "\\]\n", "\n", "---\n", "\n", "### **Answer:**\n", "\n", "It will take **5 minutes** for 100 machines to make 100 widgets.\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 analyze the problem step-by-step:\n", "\n", "- **Given:** 5 machines take 5 minutes to make 5 widgets.\n", "- **Question:** How long do 100 machines take to make 100 widgets?\n", "\n", "---\n", "\n", "### Step 1: Find the rate of one machine.\n", "\n", "If 5 machines make 5 widgets in 5 minutes, then:\n", "\n", "- Total machine-minutes to make 5 widgets = 5 machines * 5 minutes = 25 machine-minutes.\n", "- So, the time per widget per machine is \\( \\frac{25 \\text{ machine-minutes}}{5 \\text{ widgets}} = 5 \\text{ machine-minutes per widget} \\).\n", "\n", "This means **one widget requires 5 machine-minutes** to be produced.\n", "\n", "---\n", "\n", "### Step 2: Determine production with 100 machines.\n", "\n", "- Each widget requires 5 machine-minutes.\n", "- With 100 machines working in parallel, in one minute, they can do 100 machine-minutes of work.\n", "\n", "---\n", "\n", "### Step 3: Calculate the time to make 100 widgets.\n", "\n", "- Total machine-minutes needed for 100 widgets = \\(100 \\text{ widgets} \\times 5 \\text{ machine-minutes/widget} = 500 \\text{ machine-minutes}\\).\n", "- Since 100 machines can contribute 100 machine-minutes per minute, the time taken \\(t\\) is:\n", "\n", "\\[\n", "t = \\frac{500 \\text{ machine-minutes}}{100 \\text{ machine-minutes/min}} = 5 \\text{ minutes}\n", "\\]\n", "\n", "---\n", "\n", "### **Answer:**\n", "\n", "It will take **5 minutes** for 100 machines to make 100 widgets." ], "text/plain": [ "" ] }, "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": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Exercise

\n", " Now try this commercial application:
\n", " First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.
\n", " Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.
\n", " Finally have 3 third LLM call propose the Agentic AI solution.
\n", " We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Great question! Among sales-related pain points, one of the most ripe for Agentic AI intervention is:\n", "\n", "### Pain Point: **Consistently Engaging and Nurturing Leads at Scale**\n", "\n", "Salespeople often struggle to keep leads engaged over a prolonged sales cycle, especially when managing dozens or hundreds of prospects simultaneously. Manual follow-ups are tedious, easy to forget, poorly timed, or impersonal—all leading to lost opportunities.\n", "\n", "---\n", "\n", "### Why This Pain Point is Ripe for Agentic AI:\n", "\n", "- Requires ongoing, personalized communication tailored to each lead’s behavior and status.\n", "- Often repetitive tasks that take significant time but yield high ROI if done well.\n", "- Difficult to scale using human labor alone without sacrificing quality.\n", "- Involves decision-making about *when* and *how* to follow up, which messaging to use, and when to escalate or disengage.\n", "\n", "---\n", "\n", "## Proposed Agentic AI Solution: **\"LeadNurture Agent\"**\n", "\n", "### Overview:\n", "\n", "**LeadNurture Agent** is an autonomous sales assistant designed to **proactively manage lead engagement and nurturing** throughout the sales funnel. It acts intelligently and independently—deciding when to reach out, how to customize messaging, and when to push for meetings or move on.\n", "\n", "---\n", "\n", "### How LeadNurture Agent Works:\n", "\n", "1. **Lead Profiling & Segmentation**\n", " - Ingests CRM data plus third-party signals (e.g., news, social media activity, website visits).\n", " - Continuously updates lead “temperature” or engagement score dynamically.\n", "\n", "2. **Personalized Outreach Engine**\n", " - Crafts tailored communications using NLP tuned to tone, past interactions, and buyer persona.\n", " - Selects_optimal contact channels_ (email, LinkedIn, SMS) based on lead preference and response rates.\n", "\n", "3. **Autonomous Follow-Up Scheduling**\n", " - Automatically sends follow-ups at best-known intervals—adjusting cadence in real-time based on lead responses or silence.\n", " - Escalates interest to salesperson only when the lead behaves in a way indicating readiness (e.g., document downloads, demo requests).\n", "\n", "4. **Adaptive Engagement Strategy**\n", " - Shifts tactics autonomously if lead remains unresponsive (e.g., switching from soft content sharing to more direct calls to action).\n", " - Learns from each interaction outcomes using reinforcement learning.\n", "\n", "5. **Insight & Hand-Off Dashboard**\n", " - Provides salesperson with up-to-date insights into lead status and AI-driven recommendations on when to intervene.\n", " - Smoothly hands over “hot leads” with context for closing.\n", "\n", "---\n", "\n", "### Benefits:\n", "\n", "- Frees salespeople from repetitive nurturing tasks, allowing focus on closing.\n", "- Increases lead conversion rates by ensuring no lead falls through the cracks.\n", "- Scales personalized engagement beyond what human effort can achieve.\n", "- Continuously improves with experience, becoming smarter and more effective.\n", "\n", "---\n", "\n", "### Example Use Case:\n", "\n", "A salesperson has 200 leads but only the bandwidth to actively communicate with 30 at a time. LeadNurture Agent autonomously manages ongoing outreach to the remaining 170, escalating qualified and warm leads to the salesperson weekly. This results in more demos booked and higher overall quota attainment.\n", "\n", "---\n", "\n", "### Summary:\n", "\n", "**LeadNurture Agent exemplifies an Agentic AI solution by autonomously driving complex, adaptive workflows with high business impact—transforming the critical but challenging process of lead nurturing into a scalable, optimized system.**\n", "\n", "If you’d like, I can also help outline what technical components or data inputs would be necessary to build this!" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# First create the messages:\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"Can you please generate a business idea for an Agentic AI opportunity? Perhaphs something for sales people?\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "# display(Markdown(business_idea))\n", "\n", "# And repeat! In the next message, include the business idea within the message\n", "messages.append({\"role\": \"assistant\", \"content\": business_idea})\n", "messages.append({\"role\": \"user\", \"content\": \"Which pain-point is most ripe for an Agentic AI solution?\"})\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "# display(Markdown(response.choices[0].message.content))\n", "\n", "# Finally ask for an agentic AI solution\n", "messages.append({\"role\": \"user\", \"content\": \"Now, can you please propose an Agentic AI solution to the pain point?\"})\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }