Multi-Scenario Orchestration
Design patterns for showcasing one skill across 3 parallel scenarios with synchronized execution and progressive difficulty.
Core Pattern
┌─────────────────────────────────────────────────────────────────────┐
│ 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
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 patternlanggraph-parallel - Fan-out/fan-in executionlanggraph-state - State managementlanggraph-checkpoints - Persistencemulti-agent-orchestration - Coordination patterns
References
