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How does the artifact lifecycle work?

As a Pro User, you’ll actively create and manage artifacts (canvases, templates, functions, workflows). Understanding their lifecycle is crucial for effective development and deployment.

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Key Stages

  1. Creation: When you first build an artifact, it starts in a draft state, accessible to you for editing.
  2. Editing: You can continuously refine your artifacts. Each save or major change typically creates a new version.
  3. Publishing: This makes your artifact available and “live” for others. Published artifacts are what end users interact with.
  4. Versioning: MyAi automatically tracks your artifact’s change history. You can view previous versions or restore an older state, providing a robust safety net and audit trail.
  5. Archiving: When an artifact is no longer needed but its history must be retained, you can archive it. Archived artifacts are hidden from active lists but can be unarchived.

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Visualizing the Lifecycle

Beyond creating individual artifacts, MyAi lets you define explicit connections between them and your dimensions using the relationships tool. This is how you build the interconnected graph of your knowledge and processes.

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Why use Relationships?

  • Architectural clarity: Document how different parts of your system interact—for example, a workflow delegating a task to a specialized function, or a canvas pulling data from a specific template.
  • Contextual understanding: For both humans and the AI, relationships provide critical context. When the AI understands why two artifacts are connected (via from_perspective and to_perspective descriptions), it can better reason about tasks and information flow.
  • System navigation: Relationships help you and others navigate complex systems, making it easier to discover dependencies and understand work flow.

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Key Concepts

  • from_id & to_id: The artifact_ids or dimension_ids that you are linking—the source and target of your connection.
  • relationship_type: Defines the nature of the link. Common types include:
    • delegates_to — One artifact/dimension entrusts a task to another.
    • governs — One artifact (like a template) defines the structure or rules for instances.
    • implements — A function provides the logic for a specific interface or contract.
    • related_to — A general association when more specific types don’t apply.
  • from_perspective & to_perspective: Natural language descriptions explaining the purpose of the relationship from both sides. They are powerful for documenting intent.

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Example

If you create a Customer Feedback Form (a template) and a Customer Feedback Dashboard (a canvas), you might define a relationship: 
default_api.relationships(
    operation="create",
    from_id="<Customer Feedback Dashboard Canvas ID>",
    to_id="<Customer Feedback Form Template ID>",
    relationship_type="related_to",
    from_perspective="I display and visualize data collected by this template.",
    to_perspective="I provide the structured data that the Customer Feedback Dashboard uses for reporting."
)
By explicitly defining these relationships, you build a more robust, self-documenting, and intelligently interconnected MyAi system.
MyAi Relationship Graph showing connected Dimensions

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How do Templates and Instances work?

MyAi Template editor showing Canvas Tools, template variables, and Template toggle
Understanding the relationship between Templates and Instances is foundational to building structured data solutions:
  • Templates (The Blueprint): A template artifact defines the structure or schema for a type of record. It specifies the fields (global_variables), their types, and rules. Think of it as designing a form.
  • Instances (The Data Records): An instance is a specific record created by filling out that form (the template). Each instance holds the actual data values for the fields defined by its template.
This distinction is crucial for building robust data collection and reporting systems.

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Learn More

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