AI System Builder

Definition: The AI System Builder in Taskade Genesis allows you to create interconnected AI systems - not just individual apps, but complete intelligent infrastructures where agents, automations, and data work together as a unified whole.

Beyond Apps: Building Systems

An app solves one problem. A system solves many problems and creates new capabilities through the interaction of its parts.

App: A booking form that captures appointments
System: An intelligent scheduling infrastructure that manages availability, optimizes time slots, handles cancellations, sends reminders, collects feedback, and learns to predict no-shows

System Architecture

Data Layer (Projects)
The foundation - structured information that powers everything else

• Customer databases
• Transaction histories
• Knowledge bases
• Configuration settings

Intelligence Layer (Agents)
The brain - AI that analyzes, decides, and acts

• Specialized agents for different domains
• Multi-agent coordination
• Learning and adaptation
• Natural language interfaces

Execution Layer (Automations)
The muscles - workflows that make things happen

• Trigger-based responses
• Scheduled operations
• Multi-step processes
• External integrations

Interface Layer (Apps)
The face - how humans and systems interact

• Genesis-generated applications
• Dashboards and portals
• Forms and inputs
• Reports and outputs

Building AI Systems

Step 1: Map Your Domain
Identify all the entities, processes, and relationships in your business area

Step 2: Design Data Structures
Create projects that model your domain - customers, products, transactions, etc.

Step 3: Build Specialized Agents
Create agents for each functional area - sales, support, operations, analytics

Step 4: Connect with Automations
Link everything together with workflows that respond to events and maintain state

Step 5: Generate Interfaces
Use Genesis to create apps that let humans interact with your system

Step 6: Train and Evolve
Feed your system with data, monitor performance, and refine continuously

System Examples

Customer Intelligence System

• Customer database with full history
• Support agent handling inquiries
• Sales agent identifying opportunities
• Analytics agent generating insights
• Automations syncing with CRM
• Dashboards for team visibility

Operations Management System

• Inventory tracking database
• Procurement agent managing orders
• Quality agent monitoring standards
• Logistics agent optimizing delivery
• Automations alerting on issues
• Portals for vendor collaboration

Content Production System

• Content calendar and assets
• Research agent gathering information
• Writing agent drafting content
• Editor agent reviewing quality
• Automations managing workflow
• Publishing interfaces for distribution

System vs. App Comparison

Aspect	Single App	AI System
Scope	One function	Multiple integrated functions
Intelligence	Basic AI features	Multi-agent coordination
Learning	Limited	Continuous across all parts
Scalability	Add features	Add capabilities
Value	Solves a problem	Creates new possibilities

Best Practices

Start with Core: Build the essential data and agent infrastructure first
Iterate Incrementally: Add capabilities one at a time, test each addition
Monitor Everything: Track how parts interact and where bottlenecks occur
Document Connections: Map how agents, automations, and data relate
Plan for Evolution: Design systems that can grow and adapt

Related Wiki Pages: Living DNA, Autonomous Workspace, Agent Hosting, App Builder