Hi everyone,
We are moving past simple automation. The future belongs to Agentic Apps.
Most apps today are static, they do exactly what they are told, every single time. But an Agentic App? It understands the user. It learns from behavior. It evolves.
In my latest video, I built a project called "AI Chef." It’s an agent responsible for generating recipes, but not randomly. It looks at your history, remembers your preferences and interactions, and adjusts future suggestions based on your specific palate.
I built the whole thing without writing code, and now I’m going to show you the framework so you can do it too.
The Blueprint: My AX Plan Breakdown
Before I touched n8n or any AI coding tool, I mapped out the logic. Looking at the plan, here is how I structured the 4 pillars of the app:
1. The AX Flow (The Logic)
This is the brain. I mapped the path from Onboarding (capturing allergies & cuisine preference) to Learning.
The "Dynamic Button": I created logic where the main action button changes. If the user inputs text, it says "Submit." If the input is empty, it becomes "Generate Random."
The Loop: The flow doesn't end at the output. It ends at Learning. When a user rates a recipe, that data is saved to train the next generation.
2. The UX Journey (The User Story)
I visualized exactly how a user interacts with the agent.
Input: "Dinner for 4 with rice and beef."
Output: The Agent suggests "Mongolian Beef."
Refinement: The user says, "Replace green onions with something else."
Result: The Agent creates a "Mongolian Beef Twist."
Rating: User gives 5 stars → System learns "User likes Beef Twists."
3. The AI Agent Framework (The Rules)
This determines how the LLM behaves. I set strict formatting rules here.
Inputs: The agent must look at the Database (historic preferences) AND the User Prompt simultaneously.
Output Format: The AI cannot just chat. It must output structured data: Name, Servings, Duration, Type, Cuisine, Spice Level, and Ingredients.
4. Core UI Components (The Interface)
Finally, I defined the visual blocks for the frontend builder.
User Input: Text field, Voice recorder, Image upload.
Recipe Card: Needs to display the structured data (Duration, Cuisine) clearly.
Library: A place to sort saved recipes by Rating or Date Added.
The Tech Stack (The "How")
To bring this chart to life without coding, we need a brain and a body.
The Brain: n8n
I use n8n for the backend. It handles the complex "Learning" logic that tools like Zapier struggle with.
Pro Tip: Don’t pay the full $24/mo price. I self-host n8n using a Hostinger VPS. It costs about $5/mo and gives you full control. (Use code AITOOLTIP for a discount).
The Body: Lovable
I used AI coding tools to generate the UI based on the "Core UI Components" section of my chart. By giving them the Webhook URL from n8n, the UI sends the request to the agent and waits for the structured JSON response.
The Secret Sauce: Training ChatGPT
Here is the biggest hurdle I faced: ChatGPT is outdated.
If you ask ChatGPT to help you build this n8n workflow, it will give you instructions for an old version of the interface. It leads to broken nodes and errors.
So, I fixed it.
I scraped the latest n8n documentation and GitHub repositories to create a "Knowledge File."
Download the training data file below.
Open a new ChatGPT chat.
Upload the training file.
Now ask it to build the workflow from the AX Chart.
It will tell you exactly which nodes to use, what code to paste, and how to connect your Webhooks.
Your Weekly Learning Task
I want you to take the first step toward building your own Agentic App
The Challenge: The "Preference" Loop
Download the Resource: Grab the n8n documentation file I linked above.
Prime ChatGPT: Upload the file and say: "I want to build an n8n automation. Use this file to understand the latest node structures."
Generate the Logic: Ask ChatGPT to design a simple workflow: "Create a workflow that takes a user's food preference (e.g., 'Spicy') via a Webhook, checks a mock database for past meals, and suggests a new dinner idea."
Build it: If you have n8n set up, try to implement just those 3 nodes (Webhook → AI Agent → Output).
Once you understand how to make the AI look at past data before generating new data, you have mastered the core of Agentic AI.
Happy building!
