An architectural pattern where a central orchestrator coordinates the work of multiple specialized worker processes or agents. The orchestrator makes high-level decisions about task allocation, coordination, and synthesis while workers focus on executing specific subtasks.
Key Characteristics
Separation of Concerns: The orchestrator handles coordination logic while workers handle execution. This mirrors the single responsibility principle - each component has a focused purpose.
Dynamic Task Allocation: The orchestrator can spawn workers based on runtime conditions rather than predetermined workflows. This enables adaptive behavior where the system responds to intermediate results.
Parallel Execution: Multiple workers can operate simultaneously, enabling parallel processing of independent subtasks. The orchestrator manages synchronization and result aggregation.
Applications
In Multi-Agent Research Systems, the orchestrator decides which research directions to pursue while specialized agents explore individual topics. This creates emergent research capabilities beyond what single agents achieve. Open Deep Research implements this pattern at scale, demonstrating how Context Engineering enables sophisticated coordination through context isolation.
In distributed systems, orchestrators manage job queues, resource allocation, and failure recovery. Tools like Kubernetes employ this pattern to coordinate containerized workloads across compute clusters.
Tradeoffs
Benefits:
- Clear separation between coordination and execution logic
- Enables parallel processing and scale
- Flexibility to adapt based on intermediate results
- Specialized workers can optimize for specific tasks
Challenges:
- Orchestrator becomes a coordination bottleneck
- Managing state across distributed workers
- Handling worker failures gracefully
- Complexity in debugging distributed behavior
Related Patterns
Similar to but distinct from:
- Manager-Worker: Typically more static task allocation
- Map-Reduce: Specialized form focused on data transformation
- Actor Model: More decentralized, peer-to-peer coordination