Mendix AI-Assisted Development
How Does Mendix Leverage AI to Help Users to Build Applications?
Mendix leverages Artificial Intelligence (AI) and Machine Learning (ML) to help development teams model and deliver Mendix applications faster, with more consistency and with higher quality. This is an emerging trend in software development, commonly known as AI-Assisted Development (AIAD). AIAD in the Mendix platform is called Mendix AI Assistance (Maia). Maia consists of different capabilities that act as virtual co-developer capabilities, providing guidance, assistance and generation in a certain domain or stage of the application lifecycle development. Currently, Maia consists of several virtual co-developer capabilities: In Studio Pro, we have Maia Chat for developer guidance, Maia Logic and Workflow Recommenders, Best Practice Recommender for in-editor assistance, and Generative AI based features like Translation Generator. Next to this, we provide Maia Rewrite and Summarize on the Mendix Community.
How Does Mendix Leverage AI to Help Users to Build Application Logic Faster with Higher Quality?
Maia a Co-Developer
Mendix AI-assistance (Maia) guides developers during their work in Mendix Studio Pro. Developers can ask questions about app development in Mendix, including how to apply concepts, best practices, and development patterns.
Maia Chat
- Contextual Development Guidance: Developers can describe challenges in plain language, and Maia will leverage the latest documentation and community resources to provide expert guidance.
- Interactive Problem-Solving: Supports follow-up questions and maintains conversation context, allowing developers to co-develop with Maia through natural dialogue.
Next to this Maia, serves as a comprehensive generative co-developer for both data models and user interfaces, allowing developers to create and refine application components through natural language.
Domain Model Generation
- Natural Language Generation: Creates complete domain models based on simple descriptions (for example, “I need a domain model for an enterprise-ready employee training and certification application”).
- Iterative Refinement: Supports conversational refinement with follow-up requests like “Add a status field to the order entity”, in addition to expert advice like “What other entities should I consider adding to my domain model?”
Workflow Generation
- Natural Language Generation: Helps prototype advanced workflows based on natural language requests.
- Import from BPMN: Bring your existing workflows to Mendix with the power of Maia.
Page Generation and UI Design
- Design Interpretation: In addition to natural language requests, Maia can convert visual designs, sketches, or other media into functional Mendix pages with appropriate layouts and widgets.
- Component Recommendations: Suggests appropriate widgets and layout patterns based on the page’s purpose and context.
How Does Mendix Leverage AI to Help Users to Build Applications According to Mendix Best Practices?
Maia Best Practice Recommender helps improve applications by inspecting app models against Mendix development best practices. It detects anti-patterns during design and development, identifies issues, suggests resolutions, and can automatically implement fixes.
The system provides three levels of assistance:
- Detection: Inspects the model, identifies issues, and pinpoints the document/element causing the issue.
- Recommendation: Explains the identified issue, potential impact, and remediation steps, with detailed best practice guides.
- Auto-fixing: Automatically implements best practices and fixes issues.
In-editor Recommenders
Mendix enables developers to visually build application logic with microflows, nanoflows, and workflows instead of writing code. The Maia recommenders for the Page editor and all three logic editors provide AI-powered suggestions that guide users through modeling and configuring application logic. These recommenders deliver real-time, context-driven next best actions based on the application logic already designed and relevant contextual information.
Key features of Maia recommenders include:
- In-editor next best action suggestion: Recommends the top 7 next best parameterized actions
- Contextual suggestions: Derives context from surrounding elements and usage context
- Search-based suggestions: Quickly finds any parameterized action developers need
- Auto-configuration: Automates further development by pre-populating parameters
- Enhanced navigation: Combined with mouse and keyboard navigation, provides unrivaled development speed for advanced developers while helping new developers learn best practices.