Beyond Vibe Coding: Building Production-Ready Flutter and Dart Apps with AI

Over recent months, I’ve used AI day-to-day to build Flutter and Dart applications, testing different tools and seeing where they fail and succeed. This post covers the patterns that have helped me get AI-generated code to work reliably in a real codebase, the pitfalls I ran into, and how this experience is shaping the tooling we want to offer developers building product-ready Flutter and Dart apps with AI and Globe.

My Journey with AI-Assisted Development

As developers, we’re witnessing a fundamental shift in how software gets built. AI coding assistants have moved from experimental tools to productivity multipliers. As we know, there’s a critical difference between “vibe coding” your way to a prototype and using AI to build production-ready applications that scale.

I’ve been working with Generative AI models in applications since early 2024, teaching developers how to build intelligent Flutter applications with the Gemini models. But when it came to using AI agents for development itself, I approached with skepticism. My first experience with Copilot was helpful for autocompletion, but I didn’t see the transformative potential until I encountered more sophisticated tools like Cursor and Claude Code.

Building with Globe KV

My perspective changed when I used Cursor to build a proof of concept for a remote config application using Globe KV - Togglo backend and frontend. For a project where I didn’t have strict requirements around UI perfection or backend security details, the AI assistant performed remarkably well. It handled the basic architecture, generated functional code, and delivered a working prototype quickly.

This initial success made me eager to experiment further. That’s when reality set in.

Witnessing AI Agents Struggle

I initially thought I’d found a reliable junior programmer I could delegate tasks to. Instead, I discovered that AI agents have unpredictable performance patterns:

Tasks you expect them to handle well sometimes produce poor results
When agents drift off track, they rarely recover on their own
Obvious requirements frequently become non-obvious to the agent
The same model that built something from scratch might struggle with modifications

These limitations forced me to reconsider my approach entirely.

Understanding the Root Cause: Context is Everything

Through resources from developers like CodeWithAndrea and Viktor Lidholt’s excellent presentation on vibe coding, a fundamental truth became clear: context is everything.

AI models only know what they’ve been trained on. They’re not familiar with your codebase, can’t navigate your project structure, and don’t understand your specific requirements unless you explicitly provide that information. It’s like assigning a task to a junior engineer without any context - poor delivery is almost guaranteed.

What Helped Me Provide Effective Context

Based on my experience, here are the most impactful ways I’ve found to provide context:

Project Structure

Whether you’re starting fresh or working with existing code, your project itself is a fundamental context. The agent needs to understand your architecture, file organization, and coding patterns.

Rules and Documentation

Create markdown files that describe how your application behaves or should behave. For existing projects, document current behavior. For new projects, define expected behavior. Different models use specific files as permanent context - Claude uses CLAUDE.md, while other tools might use AGENTS.md. Use these files to contain application-wide rules.

Think of this documentation like writing test cases: when you fix a bug, you write a test to prevent regression. Similarly, when you correct your AI agent or encounter issues, add those corrections as additional context in your rules file.

MCP Servers

Model Context Protocol (MCP) servers provide tools that bring additional context to your development workflow. They help agents work more accurately by connecting to external resources and documentation.

As a Flutter developer, I consistently use the Dart MCP server. For the Recall app we built at Globe to showcase Globe DB, the Figma MCP server proved invaluable for building UI layers - while not 100% accurate, it reliably captured dimensional requirements like sizes and fonts.

Very Good Ventures wrote an excellent article covering 7 MCP Servers Every Dart and Flutter Developer Should Know that’s worth checking out.

The Workflow That Consistently Works for Me

After extensive experimentation, I’ve developed a workflow that consistently produces quality results:

Planning and Requirements

I get better results when I start with clear requirements - whether that’s a PRD, GitHub issue, or through iterative prompting. I then use the planning mode which popular agents like Cursor offer to generate a plan first that I can verify and modify before any code gets written. This gives me a chance to catch issues early and ensure we’re aligned on the approach.

For incremental work, I often use CLI-based approaches that mentally force me to provide context step by step: read my code, analyze this component, implement this feature.

Configuring MCP Servers

I’ve noticed that MCP servers only help when they have explicit instructions. Without them, agents often ignore available tools. In my rules file, I specify when and how to use MCP servers. For Flutter developers working with unfamiliar design systems, MCP servers can bridge the gap between design files and implementation.

Patterns I Noticed When Agents Struggle

The “I Don’t Know” Problem

AI agents rarely admit when they lack information. Ask a question about generated code, and the agent might start generating more code instead of answering.

What’s worked for me:

Maintaining written rules and keeping them current
Using MCP servers to bring additional context while rules enforce their usage
Using specific “ask” modes (like Cursor’s ask mode) designed purely for questions

Manufactured Solutions

AI models can “manufacture” solutions that don’t exist in your actual codebase. They sometimes write code that looks plausible but violates your framework’s patterns or security requirements. This is particularly common with backend code where subtle mistakes can have serious security implications.

What’s worked for me:

Reviewing all generated code carefully before integrating it
Understanding the framework’s patterns and best practices myself
Never blindly trusting generated code, especially for security-critical functionality

Where AI Excels and Where It Struggles

The Areas Where AI Consistently Saves Me Time

Through real-world usage, I’ve found AI agents excel in specific areas:

Accelerating predictable tasks: Tests, boilerplate code, and standard patterns
Exploring new ideas: Even with minimal context, agents can prototype quickly
Backend development: Custom server-side code often works better than complex Flutter UIs
Production code with proper context: When I provide clear rules and MCP servers (when needed), AI can handle significant portions of production development

What I’m Still Responsible For

AI-generated code requires active oversight:

Review and verify: Code can be incorrect or ignore best practices
Test thoroughly: Verify performance, correctness, and security
Maintain my expertise: I stay engaged with the code to keep my skills sharp

I recently attended a talk titled “Outsource Knowledge, Protect Reasoning” by Chuka Ofili. The title captures the right approach perfectly. Use AI agents to outsource knowledge retrieval while protecting your reasoning and judgment. This keeps you in control while continuously learning.

Real-World Experience with Globe

I use Globe daily, test features before release, and maintain the documentation. Through my own development work and supporting Globe users, I’ve seen AI agents consistently generate commands and patterns that don’t exist in our platform. They misuse our APIs or manufacture code that looks plausible but simply doesn’t work with Globe.

This pattern revealed a clear need: we need better ways to provide accurate Globe context to AI agents.

Ideas for Globe

I’m considering two initiatives to improve the AI-assisted development experience with Globe:

Backend Best Practices Documentation: A comprehensive set of rules for building secure, production-ready backend code following industry best practices. Which frameworks would interest you most?
Globe MCP Server: A dedicated MCP server for Globe to provide agents with accurate, up-to-date context about Globe’s APIs, patterns, and best practices.

Final Thoughts

Based on my experience, AI agents are a strong fit for Flutter and Dart development. With proper context - clear rules, well-configured MCP servers and structured workflows - they become genuinely effective development partners, not just prototyping tools.

I’m currently building a production project where AI handles about 60% of the work, and it’s going well. The key difference from my early struggles? Providing the right context and understanding where AI excels versus where I need to step in.

Here’s what I’ve observed

What works: AI agents accelerate UI scaffolding, backend glue code, tests, and repetitive work. They’re increasingly capable of production-quality code when given proper context.

What I still own: Architecture decisions, code review, testing, and security remain my responsibility.

What’s changing: As models improve and tools like MCP servers mature, I’ve been able to hand off more work to AI. The 60/40 split I’m seeing now would have seemed impossible a year ago.

Join the Conversation

How do you use AI agents for Flutter and Dart development? What patterns have you discovered? What frustrations have you encountered? Let’s continue this discussion on our Discord community.

If you’re interested in the Globe MCP server proposal or have specific backend frameworks you’d like covered in best practices documentation, I’d love to hear your feedback - reach out on X or Discord.