Orchestration
Multi-Model Coordination
Coordinate multiple AI models working together for complex workflows. Route requests intelligently, chain outputs, manage agents, and optimize resources across your AI infrastructure.
Orchestration Capabilities
Powerful tools for coordinating AI models in complex workflows
Multi-Model Routing
Intelligent routing of requests to the most appropriate model based on task requirements, model capabilities, and resource availability
Key Features
- •Automatic model selection based on query type
- •Load balancing across model instances
- •Fallback handling for model failures
- •Cost-optimized routing decisions
Workflow Chaining
Connect multiple models in sequence where the output of one becomes the input of another, creating complex AI pipelines
Key Features
- •Sequential model execution
- •Data transformation between models
- •Conditional branching based on outputs
- •Parallel execution support
Agent Coordination
Manage autonomous agents that can plan, reason, and execute tasks using multiple models and tools
Key Features
- •Multi-agent collaboration
- •Tool use and function calling
- •Planning and reasoning workflows
- •State management across executions
Resource Management
Optimize model execution across available compute resources while maintaining privacy and performance
Key Features
- •Dynamic resource allocation
- •Model instance scaling
- •Execution environment selection
- •Performance monitoring and optimization
Orchestration Patterns
Common patterns for coordinating multiple models
Sequential Pipeline
Models execute one after another in a defined sequence
Example: Document summarization → Translation → Formatting
Parallel Execution
Multiple models process the same input simultaneously
Example: Image analysis + Text extraction + Metadata generation
Conditional Branching
Route to different models based on input or intermediate results
Example: Classification → Specialized model A or B based on category
Iterative Refinement
Cycle through models multiple times to improve output quality
Example: Generate → Critique → Improve → Critique → Finalize
Real-World Use Cases
How orchestration powers complex AI applications
Creative Production
AI Film Studio orchestrates script writing, storyboarding, visual generation, and editing models
Workflow Steps
Script generation model creates screenplay
Storyboard model visualizes scenes
Image generation creates visual assets
Editing model assembles final output
Code Generation
DAML Studio coordinates code generation, analysis, testing, and optimization models
Workflow Steps
Code generation model writes smart contract
Analysis model checks for vulnerabilities
Testing model generates test cases
Optimization model improves performance
Research Assistant
Multi-model system for comprehensive research and analysis tasks
Workflow Steps
Search model retrieves relevant information
Extraction model pulls key insights
Analysis model identifies patterns
Synthesis model creates final report
Why Orchestration Matters
Complex Tasks
Break down sophisticated workflows into specialized models working together
Better Results
Specialized models excel at specific tasks, producing higher quality outputs
Resource Efficiency
Use smaller, focused models instead of one large general-purpose model
Privacy-Preserved Orchestration
Tenzro orchestration maintains privacy throughout multi-model workflows. Models can run in different environments (browser, TEE, mobile) while coordinating securely. Data transformations between models happen locally or in TEEs, never exposing intermediate results to untrusted parties.
Build Complex AI Workflows
Start orchestrating multiple models to create sophisticated AI applications