CaseStudies

Advanced Orchestration Patterns

Status: 📋 Planned
Version: v0.2.0+
Last Updated: November 15, 2025

Overview

Advanced orchestration patterns including task decomposition, parallel coordination, and adaptive agent assignment for complex multi-agent workflows that require dynamic resource allocation and intelligent task distribution.

Description

Goal

Implement sophisticated orchestration strategies that enable MassGen to handle complex, multi-stage workflows with automatic task decomposition, intelligent agent assignment based on capabilities, and efficient parallel execution.

Key Features

1. Automatic Task Decomposition
   • Break complex queries into subtasks automatically
   • Identify dependencies between subtasks
   • Generate execution DAG (Directed Acyclic Graph)
   • Support recursive decomposition for nested tasks
2. Parallel Coordination
   • Execute independent tasks simultaneously
   • Manage shared resources across parallel agents
   • Synchronize at dependency points
   • Handle partial failures gracefully
3. Adaptive Agent Assignment
   • Match tasks to agent capabilities
   • Load balancing across available agents
   • Dynamic reassignment on failure
   • Learn from successful assignments
4. Resource Management
   • Track agent availability and workload
   • Manage API rate limits across agents
   • Optimize for cost vs. speed tradeoffs
   • Support priority-based scheduling

Example Workflow

User Query: "Research AI trends, write report, create presentation"
    ↓
Task Decomposition:
├─ Research (parallel)
│  ├─ Search papers
│  ├─ Analyze GitHub trends
│  └─ Monitor Twitter discussions
├─ Write Report (depends on Research)
│  ├─ Draft sections
│  └─ Review and refine
└─ Create Presentation (depends on Report)
   ├─ Design slides
   └─ Add visualizations

Testing Guidelines

Test Scenarios

1. Simple Decomposition Test
   • Input: “Calculate 1+2 and 3+4, then multiply results”
   • Expected: Two parallel additions, followed by multiplication
   • Validation: Correct execution order, parallel execution verified
2. Complex Dependency Test
   • Input: Multi-stage data pipeline (fetch → process → analyze → visualize)
   • Expected: Proper DAG construction with correct dependencies
   • Validation: No premature execution, correct data flow
3. Adaptive Assignment Test
   • Input: Mix of tasks requiring different capabilities (coding, research, analysis)
   • Expected: Tasks assigned to appropriate agents based on strengths
   • Validation: Each agent gets tasks matching their backend strengths
4. Failure Recovery Test
   • Input: Workflow where one subtask fails
   • Expected: Retry with different agent, or skip if non-critical
   • Validation: Overall workflow completes despite failures
5. Load Balancing Test
   • Input: 10 independent parallel tasks, 3 available agents
   • Expected: Even distribution, minimal idle time
   • Validation: All agents utilized efficiently

Performance Metrics

• Speedup: Parallel execution time vs. sequential baseline
• Efficiency: Agent utilization percentage
• Success Rate: Percentage of workflows completed successfully
• Cost Optimization: Total API cost vs. minimum possible cost

Validation Criteria

• ✅ Automatic task decomposition for multi-step queries
• ✅ 3x+ speedup for highly parallel workflows
• ✅ >80% agent utilization in parallel sections
• ✅ Graceful degradation on partial failures
• ✅ Adaptive assignment improves success rate by >20%

Related Work

• Agent Task Planning (v0.1.7) - Dependency tracking foundation
• Preemption Coordination (v0.1.7) - Multi-agent workflow basics
• Map-Reduce Document Processing (Planned) - Parallel pattern example

References

See ROADMAP.md for detailed long-term vision and development timeline.