What is Agent Orchestration?
Orchestration is the coordination of multiple AI agents or complex multi-step workflows. It involves routing tasks, managing state, handling failures, and combining agent outputs.
Orchestration Patterns
Common patterns for structuring multi-agent systems.
Sequential Pipeline
Agents run in order, each processing the output of the previous one.
Parallel Execution
Multiple agents work simultaneously on different aspects of a task.
Hierarchical
A supervisor agent delegates to specialized worker agents.
Dynamic Routing
An LLM decides which agent should handle each request.
State Management
Orchestrators must track progress, intermediate results, and handle failures.
Checkpointing
Save state at key points to enable recovery from failures.
Rollback
Ability to undo steps when errors occur.
2025 Multi-Agent Patterns
These emerging patterns define how modern AI agents collaborate at enterprise scale.
Supervisor Pattern
A coordinator agent that manages and delegates tasks to specialized worker agents. The supervisor maintains the overall goal, breaks down complex tasks, and synthesizes worker outputs.
Benefits: Clear hierarchy, centralized decision-making, easier debugging
Orchestrator-Worker Pattern
A central orchestrator maintains a pool of worker agents. Workers are stateless and can be dynamically scaled. The orchestrator handles task queuing, load balancing, and result aggregation.
Benefits: Scalability, fault tolerance, resource efficiency
Handoff Mechanisms
How agents transfer control to each other. Popularized by OpenAI Agents SDK, handoffs enable seamless transitions between specialized agents while maintaining conversation context.
Explicit Handoff
Agent directly calls transfer function with target agent and context.
Condition-Based
Automatic transfer when certain conditions are met (e.g., topic detection).
Escalation
Agent transfers to more capable agent when task exceeds its scope.
Group Chat Pattern
Multiple agents collaborate on a shared problem through a structured conversation. Each agent contributes their expertise, with a moderator managing turn-taking and consensus.
Common Roles
- *Moderator: Controls flow, summarizes progress, resolves conflicts
- *Experts: Domain-specific agents that contribute specialized knowledge
- *Critic: Reviews and challenges proposals to improve quality
- *Executor: Implements the agreed-upon decisions
Pattern Comparison
Choosing the right pattern depends on your use case.
| Pattern | Complexity | Scalability | Best For |
|---|---|---|---|
| Supervisor Pattern | Medium | Medium | Structured workflows, clear task decomposition |
| Orchestrator-Worker Pattern | High | High | High-volume processing, dynamic workloads |
| Handoff Mechanisms | Low | Low | Customer service, specialized routing |
| Group Chat Pattern | High | Medium | Creative tasks, complex problem-solving |
Workflow Visualizer
Design and visualize agent workflows
Workflow Pattern
Workflow Execution
Sequential workflows execute steps one after another. Each step must complete before the next begins.
Key Takeaways
- 1Orchestration enables complex tasks through agent composition
- 2Choose patterns based on task dependencies and parallelism
- 3Robust state management is essential for reliability
- 4Monitor orchestration costs—multi-agent systems multiply API calls