Building a Health-Focused App with Pure Vibe Coding: The Mental Health Check-in System 💡
I’m thrilled to participate in the #BuildWithBuildAgent challenge, where I created a practical application using only natural language prompts. I built a scoped application called the Mental Health Check-in App, and the process was a fantastic demonstration of agentic AI. 🚀
Here’s a step-by-step walkthrough of how I designed and deployed the app on my Personal Developer Instance (PDI):
Step 1: Define the App Concept and Requirements
My goal was to provide a simple, private way for users to track their well-being and access resources:
Data Model: Created a single table: MH_CheckinLog, with fields for mood rating (1-10), stress level, sleep quality, and a notes field.
Security: Defined role-based access for mh_user (submit/view own logs) and mh_manager (view anonymous trends/admin resources).
Automation: Implemented simple business logic to flag low mood scores and a notification system for automated self-care tips.
User Experience: Created a User Portal (simple check-in form and personal history chart) and an Admin Dashboard (resource links and aggregate data).
Step 2: Prompt Build Agent
I combined these requirements into one comprehensive "vibe code" prompt:
"Create a scoped application named Mental Health Check-in App. Define a table MH CheckinLog with specified fields. Implement user and manager roles for security. Incorporate automation tasks for low mood score flags and automated self-care notifications. Finally, build a User Portal UI and an Admin Dashboard UI."
The ServiceNow Build Agent responded by generating the fully scoped app, including the table structure, security roles, and UI pages instantly.
Step 3: Debugging and Iteration (The AI Co-pilot in Action)
The initial build was smooth, but I wanted to refine the user experience.
I noticed the user portal chart wasn't displaying data exactly as I wanted. I simply fed the code snippet back into the chat with the prompt: "The chart on my user portal page is not filtering correctly for the current user. Please fix the script."
The Build Agent analyzed the logic, corrected the script to filter by the logged-in user's ID, and deployed the correction, demonstrating its true value as a debugging co-pilot.
Step 4: Conclusion
It was remarkable to see a wellness application with useful automation and a user interface deployed so quickly with minimal manual intervention. This process dramatically proves that AI handles the boilerplate, allowing developers to focus entirely on the solution architecture and human-centric problems!
Link to linked post : #buildwithbuildagent #servicenow #buildwithbuildagent #vibecoding #ai #lowcode #developer #innovatio...
#ServiceNow #BuildWithBuildAgent #VibeCoding #AI #LowCode #Developer #Innovation #MentalHealth #Wellbeing
