🎼 Orchestra Development Guide

Master multi-agent orchestration with SuperOptiX orchestras

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🎯 What Are SuperOptiX Orchestras?

SuperOptiX orchestras coordinate multiple agents to accomplish complex goals through sequential execution. Unlike individual agents that work in isolation, orchestras enable agents to:

• Collaborate sequentially - Each agent builds upon the previous agent's output
• Share context - Results from one agent become input for the next
• Maintain state - Workspace directories store intermediate results
• Execute workflows - Complex multi-step processes with dependencies

🔄 Execution Strategy Support

Tier	Execution Strategy	Parallel Tasks	Advanced Techniques
Oracles	Sequential only	1	Basic orchestration
Genies	Sequential only	1	Basic orchestration
Higher Tiers	Parallel, Mixed, Hierarchical	Multiple	Kubernetes orchestration

Note: The current version (Oracles and Genies) supports sequential execution only. Advanced orchestration features are available in higher tiers.

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🏗️ Orchestra Architecture

Core Components

graph TD
    A[Orchestra YAML] --> B[Orchestra Runner]
    B --> C[Task Scheduler]
    C --> D[Agent 1]
    D --> E[Agent 2]
    E --> F[Agent 3]

    G[Workspace] --> B
    H[Goal] --> B

    D --> G
    E --> G
    F --> G

    style A fill:#1e3a8a,stroke:#3b82f6,stroke-width:2px,color:#ffffff
    style B fill:#7c3aed,stroke:#a855f7,stroke-width:2px,color:#ffffff
    style C fill:#059669,stroke:#10b981,stroke-width:2px,color:#ffffff
    style D fill:#d97706,stroke:#f59e0b,stroke-width:2px,color:#ffffff
    style E fill:#dc2626,stroke:#ef4444,stroke-width:2px,color:#ffffff
    style F fill:#059669,stroke:#10b981,stroke-width:2px,color:#ffffff
    style G fill:#7c3aed,stroke:#a855f7,stroke-width:2px,color:#ffffff
    style H fill:#1e3a8a,stroke:#3b82f6,stroke-width:2px,color:#ffffff

Workspace Management

Orchestras use shared workspaces to maintain state between agents:

Text Only

your-project/
├── orchestra_workspaces/
│   └── your_orchestra/
│       ├── task_outputs/
│       │   ├── agent1_output.txt
│       │   ├── agent2_output.txt
│       │   └── agent3_output.txt
│       ├── intermediate/
│       │   ├── data.json
│       │   └── artifacts/
│       └── final_results/
│           └── orchestra_output.txt
└── orchestras/
    └── your_orchestra.yaml

---

🚀 Creating Your First Orchestra

1. Create an Orchestra

Bash

# Create an orchestra with existing agents
super orchestra create sdlc

# This automatically:
# - Detects existing agents in your project
# - Loads tasks from agent playbooks
# - Generates orchestra configuration
# - Creates workspace directory

2. Orchestra Configuration

The generated orchestra file (sdlc_orchestra.yaml):

YAML

metadata:
  name: "Sdlc Orchestra"
  id: "sdlc"
  version: "1.0.0"
  kind: "basic"
  description: "An orchestra to accomplish a specific goal with flexible execution strategies."

orchestra:
  id: "sdlc"
  name: "Sdlc Orchestra"
  description: "An orchestra to accomplish a specific goal with flexible execution strategies."

  # Shared workspace for stateful collaboration
  workspace:
    type: local_fs 
    path: "./orchestra_workspaces/sdlc"

  # Execution configuration (sequential only for current version)
  execution:
    strategy: "sequential"
    max_parallel_tasks: 1
    task_timeout_seconds: 300
    retry_strategy: "exponential_backoff"
    enable_metrics: true
    enable_trace: true

# Agents participating in the orchestra
agents:
  - developer
  - devops_engineer
  - qa_engineer

# Task definitions with dependencies
tasks:
  - name: implement_feature
    agent: developer
    description: >
      Apply developer expertise to '{goal}'. Implement the feature based on the provided requirement
    priority: "medium"
    timeout_seconds: 600

  - name: configure_ci_pipeline
    agent: devops_engineer
    description: >
      Apply devops engineer expertise to '{goal}'. Configure a basic CI/CD pipeline based on the project requirements
    context: ["implement_feature"]  # Depends on previous task
    priority: "medium"
    timeout_seconds: 600

  - name: create_test_plan
    agent: qa_engineer
    description: >
      Apply qa engineer expertise to '{goal}'. Create a high-level test plan including test cases for the given feature
    context: ["configure_ci_pipeline"]  # Depends on previous task
    priority: "medium"
    timeout_seconds: 600

3. Run the Orchestra

Bash

# Run with a specific goal
super orchestra run sdlc --goal "Build a simple login feature for a web application"

---

🎭 How Orchestras Work

Sequential Execution Flow

The orchestra runner executes tasks in sequence, passing results between agents:

1. Task 1 executes with the initial goal
2. Task 1 output becomes input for Task 2
3. Task 2 output becomes input for Task 3
4. This continues until all tasks are completed
5. Final result is the output from the last task

Task Execution Process

1. Task Loading: Orchestra runner loads task definitions from YAML
2. Dependency Resolution: Determines execution order based on context dependencies
3. Agent Execution: Each task runs its assigned agent with current input
4. Result Passing: Output from one task becomes input for the next
5. Workspace Storage: Intermediate results are saved to workspace directory

Workspace Integration

The orchestra runner automatically manages workspace directories:

• Creates workspace when orchestra starts
• Saves task outputs to individual files
• Maintains state between task executions
• Provides access to intermediate results

---

📋 Orchestra Examples

Example 1: Software Development Lifecycle

YAML

# sdlc_orchestra.yaml
metadata:
  name: "Software Development Lifecycle"
  id: "sdlc"

orchestra:
  workspace:
    type: local_fs 
    path: "./orchestra_workspaces/sdlc"
  execution:
    strategy: "sequential"
    max_parallel_tasks: 1

agents:
  - developer
  - devops_engineer
  - qa_engineer

tasks:
  - name: implement_feature
    agent: developer
    description: "Implement the feature based on requirements"

  - name: configure_ci_pipeline
    agent: devops_engineer
    description: "Set up CI/CD pipeline for the implemented feature"
    context: ["implement_feature"]

  - name: create_test_plan
    agent: qa_engineer
    description: "Create comprehensive test plan for the feature"
    context: ["configure_ci_pipeline"]

Usage:

Bash

super orchestra run sdlc --goal "Build a user authentication system"

Example 2: Content Creation Pipeline

YAML

# content_creation_orchestra.yaml
metadata:
  name: "Content Creation Pipeline"
  id: "content_creation"

orchestra:
  workspace:
    type: local_fs 
    path: "./orchestra_workspaces/content_creation"
  execution:
    strategy: "sequential"
    max_parallel_tasks: 1

agents:
  - researcher
  - writer
  - editor

tasks:
  - name: research_topic
    agent: researcher
    description: "Research the given topic thoroughly"

  - name: write_content
    agent: writer
    description: "Write comprehensive content based on research"
    context: ["research_topic"]

  - name: edit_and_polish
    agent: editor
    description: "Edit and polish the written content"
    context: ["write_content"]

Usage:

Bash

super orchestra run content_creation --goal "Create a comprehensive guide on machine learning"

Example 3: Data Analysis Workflow

YAML

# data_analysis_orchestra.yaml
metadata:
  name: "Data Analysis Workflow"
  id: "data_analysis"

orchestra:
  workspace:
    type: local_fs 
    path: "./orchestra_workspaces/data_analysis"
  execution:
    strategy: "sequential"
    max_parallel_tasks: 1

agents:
  - data_collector
  - data_analyzer
  - report_generator

tasks:
  - name: collect_data
    agent: data_collector
    description: "Collect and prepare data for analysis"

  - name: analyze_data
    agent: data_analyzer
    description: "Perform statistical analysis on the collected data"
    context: ["collect_data"]

  - name: generate_report
    agent: report_generator
    description: "Generate comprehensive report with insights"
    context: ["analyze_data"]

Usage:

Bash

super orchestra run data_analysis --goal "Analyze customer satisfaction survey data"

---

🔧 Customizing Orchestras

1. Modify Task Descriptions

Make task descriptions more specific to your goal:

YAML

tasks:
  - name: implement_feature
    agent: developer
    description: >
      Implement a secure user authentication system with the following requirements:
      - User registration with email verification
      - Secure password hashing
      - JWT token-based authentication
      - Password reset functionality
      Use modern security best practices and provide clear documentation.

2. Adjust Task Priorities

Set task priorities to control execution order:

YAML

tasks:
  - name: critical_feature
    agent: developer
    priority: "high"
    description: "Implement critical security feature"

  - name: nice_to_have
    agent: developer
    priority: "low"
    description: "Add optional UI enhancements"

3. Configure Timeouts

Set appropriate timeouts for different task types:

YAML

tasks:
  - name: quick_validation
    agent: validator
    timeout_seconds: 60
    description: "Quick validation check"

  - name: complex_analysis
    agent: analyzer
    timeout_seconds: 1800  # 30 minutes
    description: "Complex data analysis"

---

📁 Workspace Management

Workspace Structure

Orchestra workspaces are automatically created and managed:

Text Only

orchestra_workspaces/
└── your_orchestra/
    ├── task_outputs/           # Individual task results
    │   ├── task1_output.txt
    │   ├── task2_output.txt
    │   └── task3_output.txt
    ├── intermediate/           # Shared data between tasks
    │   ├── shared_data.json
    │   └── artifacts/
    └── final_results/          # Final orchestra output
        └── orchestra_output.txt

Accessing Workspace Data

Python

# In your custom tools or agents
import json
from pathlib import Path

def read_workspace_data(orchestra_name: str, filename: str):
    """Read data from orchestra workspace."""
    workspace_path = Path(f"./orchestra_workspaces/{orchestra_name}")
    file_path = workspace_path / "intermediate" / filename

    if file_path.exists():
        with open(file_path, 'r') as f:
            return json.load(f)
    return None

def write_workspace_data(orchestra_name: str, filename: str, data: dict):
    """Write data to orchestra workspace."""
    workspace_path = Path(f"./orchestra_workspaces/{orchestra_name}")
    intermediate_path = workspace_path / "intermediate"
    intermediate_path.mkdir(parents=True, exist_ok=True)

    file_path = intermediate_path / filename
    with open(file_path, 'w') as f:
        json.dump(data, f, indent=2)

Workspace Cleanup

Bash

# Clean up workspace after orchestra completion
rm -rf orchestra_workspaces/your_orchestra/

# Or keep for analysis
ls -la orchestra_workspaces/your_orchestra/

---

🎯 Best Practices

1. Agent Preparation

Before running orchestras, ensure your agents are optimized:

Bash

# Compile all agents
super agent compile --all

# Evaluate baseline performance
super agent evaluate developer
super agent evaluate devops_engineer
super agent evaluate qa_engineer

# Optimize agents
super agent optimize developer
super agent optimize devops_engineer
super agent optimize qa_engineer

# Re-evaluate after optimization
super agent evaluate developer
super agent evaluate devops_engineer
super agent evaluate qa_engineer

2. Goal Definition

Write clear, specific goals for better orchestration results:

Bash

# Good: Specific and actionable
super orchestra run sdlc --goal "Build a secure user authentication system with JWT tokens, password hashing, and email verification"

# Avoid: Too vague
super orchestra run sdlc --goal "Make a login system"

3. Task Dependencies

Design logical task dependencies:

YAML

tasks:
  - name: gather_requirements
    agent: analyst
    description: "Gather and document requirements"

  - name: design_architecture
    agent: architect
    description: "Design system architecture"
    context: ["gather_requirements"]  # Depends on requirements

  - name: implement_system
    agent: developer
    description: "Implement the system"
    context: ["design_architecture"]  # Depends on architecture

4. Error Handling

Monitor orchestra execution and handle failures:

Bash

# Run with verbose output for debugging
super orchestra run sdlc --goal "your goal" --verbose

# Check workspace for partial results
ls -la orchestra_workspaces/sdlc/task_outputs/

---

🔍 Monitoring and Debugging

Execution Monitoring

The orchestra runner provides detailed execution information:

Bash

super orchestra run sdlc --goal "your goal" --verbose

Output includes: - Task execution order - Execution times - Success/failure status - Workspace location - Tier validation results

Workspace Analysis

After orchestra completion, analyze the workspace:

Bash

# View task outputs
cat orchestra_workspaces/sdlc/task_outputs/implement_feature_output.txt
cat orchestra_workspaces/sdlc/task_outputs/configure_ci_pipeline_output.txt
cat orchestra_workspaces/sdlc/task_outputs/create_test_plan_output.txt

# View final results
cat orchestra_workspaces/sdlc/final_results/orchestra_output.txt

Debugging Failed Orchestras

Bash

# Check which task failed
ls -la orchestra_workspaces/sdlc/task_outputs/

# View error logs
cat orchestra_workspaces/sdlc/task_outputs/failed_task_output.txt

# Re-run with dry-run to validate configuration
super orchestra run sdlc --goal "your goal" --dry-run

---

🚀 Advanced Techniques (Higher Tiers)

Note: These features are available in higher tiers beyond Oracles and Genies.

Parallel Execution

YAML

execution:
  strategy: "parallel"
  max_parallel_tasks: 3

Mixed Strategy

YAML

execution:
  strategy: "mixed"
  task_groups:
    - name: "research_phase"
      tasks: ["gather_data", "analyze_requirements"]
      execution_strategy: "parallel"
    - name: "development_phase"
      tasks: ["implement", "test"]
      execution_strategy: "sequential"

Kubernetes Orchestration

YAML

orchestration_type: "kubernetes"
replicas: 3
resources:
  cpu: "1000m"
  memory: "2Gi"

---

🎯 Next Steps

1. Create your first orchestra: super orchestra create my_orchestra
2. Customize the configuration: Edit the generated YAML file
3. Run with a specific goal: super orchestra run my_orchestra --goal "your goal"
4. Analyze results: Check the workspace directory
5. Optimize agents: Improve individual agent performance
6. Iterate and improve: Refine orchestra configuration based on results

---

🔗 Related Resources

• Agent Development Guide - Learn how to create agents for orchestration
• Tool Development - Create tools that work with orchestras
• SuperSpec DSL Guide - Configure agents for orchestration
• CLI Reference - Orchestra command reference
• Quick Start Guide - Complete orchestra example

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Ready to orchestrate multiple agents for complex goals? Start with sequential execution and build powerful multi-agent workflows! 🎼