Orchestration Pattern Multi-Agent Task Delegation V2

The Orchestrator-Worker Pattern: Multi-Agent Task Delegation and Result Aggregation

In the Orchestrator-Worker pattern, a coordinator agent receives a complex request, decomposes it into sub-tasks, assigns each sub-task to a specialist worker agent using Assign a Task to Genie, collects results, and synthesizes a unified response — all within the same user session.

How It Works

The orchestrator Genie receives the user's request and determines which specialist agents are needed. It uses the Assign a Task to Genie action to delegate each sub-task to the appropriate worker Genie. The worker executes the task and returns a result. The orchestrator collects all results and synthesizes a final response to the user.

Unlike the Delegate/Handover pattern, the user stays in the same conversation session throughout. The orchestration happens behind the scenes.

Workato Implementation

The Assign a Task to Genie action is the core mechanism. Configure it in the orchestrator's Skills or App Event recipes to call a named worker Genie with a specific task description and any input data the worker needs.

Current platform limitations: Worker Genies invoked via Assign a Task to Genie currently cannot access user context, runtime connections, permission-aware knowledge bases, or user confirmation flows. Design worker agents to operate on data passed explicitly by the orchestrator rather than relying on user-scoped resources.

MCP Server Orchestration Variant

The Orchestrator-Worker pattern also applies to MCP servers. A Federated MCP Server acts as the orchestrator, receiving requests from MCP clients and routing them to appropriate downstream MCP servers based on domain or capability.

  • The MCP client must have explicit instructions to use the Federated MCP Server as its entry point.
  • The Federated MCP Server exposes tools that describe available downstream servers and when to use each.
  • Downstream MCP servers operate independently; the federated layer handles routing only.

When to Use This Pattern

Data table
Good fitPoor fit
Task has clear sub-task boundaries across domains (e.g., HR + IT + Finance lookup)Sub-tasks require user confirmation or runtime user connections
Results from sub-tasks can be synthesized without additional user inputWorker agents need to access permission-aware knowledge bases
User experience benefits from a single-session responseSub-tasks have high interdependency (output of one feeds input of next in real-time)

Design Checklist

  • Worker Genies are scoped to a single domain with no reliance on user-scoped connections
  • Orchestrator passes all required inputs explicitly in the Assign a Task payload
  • Each worker has a clear, single output format the orchestrator can reliably parse
  • Error handling is defined for worker failures — orchestrator knows how to respond if a worker Genie fails or times out
  • The Federated MCP variant has a catch-all response when no downstream server matches the request

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