This post details a hands-on exercise focused on integrating comprehensive frontend validation into an existing full-stack application using the Google Antigravity IDE and its powerful Code Agent feature. The goal was to sequentially implement several crucial validation rules to enhance user experience and data integrity across various authentication flows, all driven by natural language prompts to the AI agent.
1. Project Scaffolding and Toolset 
Our starting point was a base full-stack application, whose code structure and initial state were derived from a pre-existing tutorial project. This provided a live application environment where we could immediately observe the agent’s impact.
The primary tool for this exercise was the Google Antigravity IDE, leveraging its integrated AI Coding Agent—accessible via the Ctrl+L code with agent command. This feature allows developers to describe the required code changes in plain language, trusting the agent to analyze the project context and execute the necessary modifications.
2. Agent-Driven Validation Implementation 
We proceeded through the validation requirements one by one, focusing on the agent’s ability to interpret generic prompts, identify affected components, and correctly implement the logic.
Task 1: Comprehensive Email Format Validation 
The first step was to enforce a valid email format across all relevant forms. A key observation was that the email field was present in both the Signup and Reset Password components.
We provided the agent with a single, generic prompt:
“Add validation to check that the email is in a proper format.”
The agent demonstrated its contextual awareness by successfully:
- Identifying the email input field across multiple files (specifically within the Signup and Reset Password screens).
- Implementing the requisite regular expression or library-based validation logic (e.g., using a schema validation library or HTML5 input types) in both components simultaneously.
This highlights the agent’s capability for efficient, multi-file updates based on a single, high-level instruction.
Task 2: Login Form Mandates 
Next, we focused on ensuring user credentials were provided before allowing a login attempt.
Required Validation:
- Username is mandatory.
- Password is mandatory.
Agent Prompt:
“User name and password mandate before clicking the login button.”
The agent implemented the logic to disable the login button or display an error message if the respective input fields were empty upon submission, ensuring the form’s dirty state and validity state were correctly managed.
Task 3: Signup Form Mandates 
The signup process typically requires more information, demanding stricter validation.
Required Validation:
- Username is mandatory.
- Email is mandatory.
- Password is mandatory.
Agent Prompt:
“User name, email and password mandate before signup the user.”
The agent added the required field validation for all three inputs in the signup form, reinforcing that the agent successfully applies tailored validation rules specific to the form’s context, even after previous, more generic validations (like email format) were already added.
Task 4: Reset Password Mandates 
Finally, we addressed the validation necessary for the password recovery flow.
Required Validation:
- Email is mandatory.
- New Password is mandatory.
Agent Prompt:
“Email and new password mandate before clicking reset password.”
This change ensured that a user could not proceed with the password reset without providing both the account email and the new credentials, reinforcing security and preventing submission of incomplete form data.
3. Conclusion and Functional Verification 
Following the sequential implementation of all four validation tasks, the final crucial step was to perform a comprehensive functional test of the entire application.
The goal of this verification was to confirm two things:
- All new validation rules (format checks and required fields) were working correctly across the Login, Signup, and Reset Password flows.
- The agent’s changes had not introduced any breaking changes or regressions into the existing application functionality.
The successful completion of this exercise confirmed the efficacy of using an AI Coding Agent for fast, context-aware implementation of declarative validation logic. The Google Antigravity Agent proved capable of handling both focused, form-specific rules and broad, multi-file updates from a single natural language instruction, significantly accelerating the development process for front-end validation.
No comments:
Post a Comment