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Orchestrator-Workers

One model plans the sub-tasks at runtime, workers run them, and the orchestrator combines the results. Its three failure modes and the fix.

2 min readLast updated

In the orchestrator-workers pattern, one model plans and other model calls carry out the plan.

How it works#

The orchestrator splits the task into sub-tasks, each worker runs one, and the orchestrator combines the results; a check sits after the plan and after each worker
  1. Plan. The orchestrator receives the task and breaks it into a list of sub-tasks.
  2. Work. Each worker receives one sub-task and carries it out. Workers often run in parallel.
  3. Synthesize. The orchestrator takes all the worker results and writes the final answer.

Example: a competitive analysis of five companies. The orchestrator splits it into five sub-tasks, one per company. Five workers run in parallel, each researching one company. The orchestrator reads the five results and writes the final comparison.

How it differs from chaining#

Prompt chaining Orchestrator-workers
Who decides the steps Your code, ahead of time The orchestrator model, at runtime
Flexibility Low: the same steps every time High: the plan fits each task
Predictability High Lower: the plan depends on what the orchestrator decides

Your code still runs the pipeline: plan, work, synthesize. Only the list of sub-tasks comes from the model.

Three failure modes#

Failure What happens
Wrong plan The orchestrator splits the task badly, and every worker inherits the mistake.
Silent worker failure A worker returns a weak or empty result, and the orchestrator synthesizes from it anyway.
Lossy synthesis The final answer drops details that were in the worker outputs.

The fix: check every handoff#

  • Check the plan before you dispatch any workers. Is each sub-task clear? Is anything missing or duplicated?
  • Check each worker result before it goes to synthesis. Retry or flag a weak result instead of passing it on.

Short line#

In orchestrator-workers, one model breaks the task into sub-tasks at runtime, workers run them, and the orchestrator combines the results. It is more flexible than chaining but less predictable, so check the plan before dispatch and each worker result before synthesis.

Key takeaways#

  • The orchestrator plans, workers execute, and the orchestrator synthesizes.
  • In chaining your code decides the steps. Here the orchestrator decides them at runtime.
  • More flexible, less predictable.
  • Failure modes: a wrong plan, silent worker failures, and synthesis that loses details.
  • Validate at each handoff: the plan before dispatch, each result before synthesis.
Revision notes · 1 min

Core idea#

One model plans, other model calls execute. The orchestrator decides the sub-tasks at runtime.

Steps#

  1. The orchestrator breaks the task into sub-tasks.
  2. Each worker runs one sub-task, often in parallel.
  3. The orchestrator synthesizes the final answer.

Example: compare five companies. Five workers each research one. The orchestrator writes the comparison.

Compare with chaining#

Who decides the steps Predictability
Prompt chaining Your code, ahead of time High
Orchestrator-workers The orchestrator, at runtime Lower

Pitfalls#

  • A wrong plan: every worker inherits it.
  • Silent worker failure: synthesis uses weak results anyway.
  • Lossy synthesis: details from workers get dropped.
  • Fix: check the plan before dispatch. Check each worker result before synthesis.

Short line#

A model plans sub-tasks, workers run them, the orchestrator combines. Check every handoff.

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