Complete AI Agent Practical Course C| AIPC

Discover how an AI agent functions as a digital teammate with brain, memory, and tools to think, remember, and act.

Explore how the brain, powered by large language models, enables agents to understand natural language, reason, plan multi-step actions, and generate dynamic responses while highlighting prompt engineering and limitations.

Explore how memory, including short-term and long-term storage, empowers AI agents to remember preferences and history, using vector stores like Pinecone and tools like n8n to become planners and coaches.

Explore how ai agents fuse a brain (llm), memory, and real-world tools to think, remember, and act—using web browsing, application programming interfaces, emails, databases, and code execution.

Discover no-code platforms that empower AI agents to connect tools, manage memory, and act through drag-and-drop workflows. Explore n8n, Make.com, Zapier, and Flow wise for building intelligent agents.

Explore how n8n acts as the engine powering AI agents, connecting models to tools like Gmail and Google Sheets, automating workflows in a free open source platform.

Build your first ai agent workflow in n8n by creating a daily weather update that fetches live data via an API and emails the latest temperature.

Connect to OpenWeatherMap to fetch current weather data by creating an API key, selecting current weather, and setting city London with metric Celsius; explore input, output, and JSON views.

Build an AI-powered workflow that automatically fetches weather data from Open Weather Map and emails a daily HTML weather report via Gmail at 8 a.m.

Design and implement a dynamic lead processing workflow in n8n that collects form inputs, uses smart conditional logic, and automates sponsorship communications with Google Sheets and Gmail integration.

Build a dynamic lead processing workflow with a no-code system that captures form data, evaluates it with logic, and automates personalized communication from the on form submission trigger.

Learn to test a sponsorship form in n8n by submitting sample data, viewing raw output in schema, table, and json views, and pinning data to preserve test inputs.

Add a switch node to your workflow to evaluate the budget and route into high- and low-priority paths using a 500,000 threshold, enabling personalized follow-up emails based on form input.

Connect logic to Gmail to automatically send personalized emails based on sponsor budget, using form inputs like company name, contact name, and email, with dynamic subject and message.

Connect Google Sheets to your N8N workflow and append a new row for every form submission, creating a real-time sponsor inquiry tracker with mapped fields and follow-up decisions.

Builds and tests an end-to-end sponsorship workflow that branches by budget, sends automated emails for high-priority leads, and logs submissions in a Google Sheet.

Design a reusable automation blueprint that collects form data, decides with logic, sends personalized emails, and records results in google sheets for scalable real-world workflows.

Build the Intelligent Agent Integration System to fuse conversation, memory, and tools, enabling real-time reasoning with Gmail, Google Calendar, and Google Sheets in a no-code workflow.

Set up a chat message trigger to start a conversational workflow, connect an AI agent with brain, memory, and tools, and configure an OpenAI API key credential for secure integration.

Set up simple memory to enable your AI agent to remember context and past interactions, making conversations continuous.

Test your intelligent agent integration by validating chat trigger, memory, and chat model communication, diagnose quota and billing issues, and ensure end-to-end workflow visibility.

Equip your AI agent with Gmail, Google Calendar, and Google Sheets to read emails, check your schedule, and access sponsorship data, enabling proactive, real-world automation.

Craft a system prompt to define the agent’s role and personality as a personal assistant. Outline tools Gmail, calendar, Google Sheets and how the agent uses them with clear boundaries.

Demonstrate end-to-end reasoning and execution of a conversational ai agent that uses memory and Gmail, Google Calendar, and Google Sheets to provide live data insights.

Create a real-time inventory management AI that reads and updates a Google Sheets stock database through a natural-language chat interface, using memory and OpenAI integration for contextual, actionable insights.

Set up an inventory AI workflow with a chat trigger, AI agent, and memory to hold context, and learn how memory and context windows create connected responses in inventory management.

Connect your AI agent to a live Google Sheet to read inventory data using n8n, including stock quantities. This data-driven framework enables real-time stock queries and out-of-stock alerts without code.

Teach your AI to act on inventory data by updating Google Sheets with append or update row, matching on item name and quantity in stock via natural language commands.

Explore how real-time updates, intelligent memory, and a professional chat interface empower an AI agent to read data, reason, and automatically add or update inventory in Google Sheets.

Develop inbox intelligence with an ai-powered email triage and reply system that watches Gmail, classifies messages, and drafts professional replies using an Open AI model.

Set up a Gmail trigger in n8n that wakes the inbox-powered AI agent on new messages, detects sponsorship or high-priority emails, and prepares personalized drafts.

Learn to classify incoming emails with a text classifier to distinguish sponsorship inquiries from high-priority messages. Enable automated drafts by wiring OpenAI-powered actions to the Gmail trigger.

Automate email responses by wiring a Gmail trigger to a text classifier and an AI model, then draft the generated subject and body into Gmail drafts.

Debug and refine an AI email assistant to automate drafting in Gmail. Build an end-to-end workflow that classifies emails, generates JSON structured responses, and creates drafts while handling real-world errors.

Learn to build a real business chatbot on WhatsApp using an end-to-end AI agent that handles orders, inventory checks, FAQs, and live customer conversations via the WhatsApp Business Cloud API.

Set up a WhatsApp restaurant automation workflow that handles messages, inventory, FAQs, and orders using an AI agent with memory and the Gemini chat model, connected to Google Sheets.

Expand the core engine by connecting memory, Gemini chat model, and Google Sheet tools for inventory, FAQs, and order recording, while refining prompt clarity.

Place real orders by connecting inventory, FAQ, and orders sheets; shape AI behavior with a system message, learn append-row order storage, and implement cancellation workflows.

Connect your AI agent workflow to WhatsApp via the WhatsApp Business Cloud API, configure credentials, and enable real customer conversations with a test-ready restaurant ordering bot.

Connect your AI agent to WhatsApp, wire real messages through a memory-enabled workflow, and generate live orders written to a Google Sheet via send message and wait for response.

Build a live ai dashboard that reads and updates business data from Google Sheets, visualizes insights as charts, and uses a webhook and chatbot to automate actions and emails.

Explore how structured data, a real-time dashboard, and a fully connected AI agent automate order tracking, revenue analysis, and client outreach using Lovable AI and Google Sheets.

Connect the AI agent to your Google Sheet and lovable dashboard in n8n, creating an automation layer that makes the dashboard interactive and a real time AI operations assistant.

Transform the workflow into a live webhook-based system by replacing the chat trigger with a post webhook and a respond to webhook node, enabling the dashboard and Gmail-enabled emails.

Embed a fully functional chatbot inside the dashboard to converse with the ai agent, read and update live data, and drive end-to-end automation with tools like Google Sheets and Gmail.

Discover how a Rag agent uses retrieval augmented generation with Supabase as a vector store and Gemini embeddings to ground answers in large documents.

Explore how retrieval augmented generation (rag) fuels deep knowledge agents and how Supabase stores embeddings in a vector database to enable grounded, scalable answers.

Add Supabase vector store to the workflow, loading a PDF, chunking text into 1000-character pieces with 200-character overlap, generating embeddings with Google Gemini, and storing them for a rag system.

Resolve embedding dimensionality mismatch by adjusting the Supabase table from 1536 to 768, then store 306 768-dimensional vectors, enabling a scalable rag retrieval workflow from the PDF.

Bring a rag agent to life by connecting an ai agent to Superbase, adding a chat interface, and enabling persistent memory for grounded, end-to-end retrieval.