Multi-Scenario Orchestration
Design patterns for showcasing one skill across 3 parallel scenarios with synchronized execution and progressive difficulty.
Core Pattern
code
┌─────────────────────────────────────────────────────────────────────┐ │ MULTI-SCENARIO ORCHESTRATOR │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ [Coordinator] ──┬─→ [Scenario 1: Simple] (Easy) │ │ ▲ │ └─→ [Skill Instance 1] │ │ │ ├─→ [Scenario 2: Medium] (Intermediate) │ │ │ │ └─→ [Skill Instance 2] │ │ │ └─→ [Scenario 3: Complex] (Advanced) │ │ │ └─→ [Skill Instance 3] │ │ │ │ │ [State Manager] ◄──── All instances report progress │ │ [Aggregator] ─→ Cross-scenario synthesis │ │ │ └─────────────────────────────────────────────────────────────────────┘
When to Use
| Scenario | Example |
|---|---|
| Skill demos | Show /implement on simple, medium, complex tasks |
| Progressive testing | Validate skill scales with complexity |
| Comparative analysis | How does approach differ by difficulty? |
| Training/tutorials | Show skill progression from easy to hard |
Quick Start
python
from langgraph.graph import StateGraph
# 1. Define 3 scenarios with progressive difficulty
scenarios = [
{"name": "simple", "complexity": 1.0, "input_size": 10},
{"name": "medium", "complexity": 3.0, "input_size": 50},
{"name": "complex", "complexity": 8.0, "input_size": 200},
]
# 2. Fan out to parallel execution
# 3. Aggregate results
# 4. Report comparative metrics
Scenario Difficulty Scaling
| Level | Complexity | Input Size | Time Budget | Quality |
|---|---|---|---|---|
| Simple | 1x | Small (10) | 30s | Basic |
| Medium | 3x | Medium (50) | 90s | Good |
| Complex | 8x | Large (200) | 300s | Excellent |
Synchronization Modes
| Mode | Description | Use When |
|---|---|---|
| Free-running | All run independently | Demo videos |
| Milestone-sync | Wait at 30%, 70%, 100% | Comparative analysis |
| Lock-step | All proceed together | Training |
Key Components
- •Coordinator - Spawns and monitors 3 instances
- •State Manager - Tracks progress per scenario
- •Aggregator - Merges results, extracts patterns
Key Decisions
| Decision | Recommendation |
|---|---|
| Synchronization mode | Free-running with checkpoints |
| Scenario count | Always 3: simple, medium, complex |
| Input scaling | 1x, 3x, 8x (exponential) |
| Time budgets | 30s, 90s, 300s |
| Checkpoint frequency | Every milestone + completion |
Common Mistakes
- •Sequential instead of parallel: Defeats purpose. Always fan-out.
- •No synchronization: Results appear disjointed.
- •Unclear difficulty scaling: Differ in scale, not approach.
- •Missing aggregation: Individual results lack comparative insights.
Related Skills
- •
langgraph-supervisor- Supervisor routing pattern - •
langgraph-parallel- Fan-out/fan-in execution - •
langgraph-state- State management - •
langgraph-checkpoints- Persistence - •
multi-agent-orchestration- Coordination patterns
References
- •Architectural Patterns - Full architecture
- •State Machine Design - LangGraph state
- •LangGraph Implementation - Code examples
- •Claude Code Instance Management - Multi-instance
- •Skill-Agnostic Template - Reusable template