Advanced Data Models

Agents Creating Dynamic Data Models from App Manifests

Watch AI agents automatically transform your app manifest data model definitions into rich, structured data. Agents populate fields, create relationships, and manage complex data workflows—all through simple YAML configuration.

Advanced Guide View Examples

Agents Creating Dynamic Data Models

Watch AI agents automatically create, populate, and manage dynamic data models defined in your app manifest

Content Generation App Manifest
manifest.yaml
app_manifest:
  name: content-generator
  description: AI-powered content creation with dynamic data models

data_models:
  - name: Content Pieces
    model_slug: content_pieces
    description: AI-generated content with metadata and relationships
    fields:
      - name: ID
        field_column: id
        type: uuid
        required: true
        is_system_field: true
      - name: Title
        field_column: title
        type: string
        required: true
        description: Content title generated by AI
      - name: Content Body
        field_column: content
        type: text
        required: true
        description: Main content generated by agent
      - name: Content Type
        field_column: content_type
        type: string
        required: true
        choices: [blog_post, social_media, email, product_description]
        description: Type of content generated
      - name: Target Audience
        field_column: target_audience
        type: string
        required: false
        description: Intended audience for the content
      - name: Keywords
        field_column: keywords
        type: json
        required: false
        description: SEO keywords and tags
      - name: Content Status
        field_column: status
        type: string
        required: true
        default: draft
        choices: [draft, review, approved, published]
        description: Current status in workflow
      - name: AI Model Used
        field_column: ai_model
        type: string
        required: false
        description: Which AI model generated this content
      - name: Generation Prompt
        field_column: prompt
        type: text
        required: false
        description: Original prompt used for generation
      - name: Quality Score
        field_column: quality_score
        type: float
        required: false
        description: AI-assessed content quality score
      - name: Created At
        field_column: created_at
        type: datetime
        required: true
        auto_now_add: true
        is_system_field: true

Dynamic Data Creation in Action

Agent-Generated Content

AI agents automatically create content records with rich metadata and relationships

Structured Data Output

Transform unstructured AI outputs into structured, queryable data models

Analytics & Insights

Analyze content performance, quality scores, and generation patterns across all AI-created data

Workflow Integration

Seamlessly integrate generated content into approval workflows and publishing pipelines

Agent-Driven Data Relationships

Watch agents create complex data relationships automatically through your app's data model definitions

Content Pieces

title: string
content: text
content_type: choices
quality_score: float
campaign_id: foreign_key
belongs to

Marketing Campaigns

campaign_name: string
target_audience: string
start_date: datetime
budget: decimal
status: choices
tracks

Performance Analytics

content_id: foreign_key
views: integer
engagement_rate: decimal
conversion_rate: decimal
roi: decimal

Real-World Example: AI-Driven Marketing Content Pipeline

1

Campaign Creation Agent

Creates campaign record with target audience analysis

campaign: "Q4 Product Launch", audience: "tech_professionals"
2

Content Generation Agent

Generates multiple content pieces linked to campaign

blog_posts: 5, social_media: 12, emails: 8
3

Analytics Agent

Tracks performance metrics and ROI for each content piece

total_views: 15.2k, engagement: 8.3%, roi: 245%

Advanced Data Modeling Features

Leverage sophisticated data patterns for complex AI applications

Execution Trees

Model parent-child relationships between agent executions to build complex workflow trees with full traceability

Research Coordinator
Topic Analysis
Data Collection
Content Generation
Draft Writing
Image Creation

Versioned Outputs

Track multiple versions of agent outputs with automatic versioning and change detection

v3.2 Current 2 hours ago
v3.1 Previous 1 day ago
v3.0 Archive 3 days ago

Semantic Search

Search across agent outputs using semantic similarity and content understanding

94% AI Automation Best Practices
87% Workflow Optimization Guide

Analytics & Insights

Built-in analytics for agent performance, cost tracking, and output quality metrics

$24.50
This Month
1,247
Executions
4.8s
Avg Time

See Advanced Models in Action

Watch how complex agent workflows create structured data automatically

Agent Executions 47
Generated Content 23
Workflow States 8
Performance

Recent Agent Executions

Content Generator

2 minutes ago

Generated 3 blog posts about AI automation trends

4.2s $0.23 3 outputs

Research Agent

Running for 1m 23s

Analyzing market trends for Q4 2024 report

Workflow Coordinator

15 minutes ago

Orchestrated multi-step content creation pipeline

12.7s $1.45 5 sub-agents

Ready to Build Advanced AI Applications?

Learn how to model complex agent workflows and outputs as structured data

Advanced Models Guide API Reference