What is the orchestrator vs worker pattern?

One agent (orchestrator) plans, delegates subtasks, and aggregates results; one or more workers each execute a focused subtask and report back in a fixed format. Separating roles keeps each prompt small and reliable.

Why does a shared handoff format matter?

If the orchestrator and workers do not agree on message structure, results get misparsed and the loop drifts. A fixed JSON or tagged format lets each side reliably read the other's output.

How do I stop an infinite agent loop?

Define an explicit termination condition — a max iteration count, a done signal, or a goal-met check. The generated orchestrator prompt enforces it so the agents stop instead of looping forever.

Can I use this for more than two agents?

Yes. List all worker roles; the worker prompt is written to be parameterized by role, and the orchestrator prompt routes to each role by name through the same protocol.

Does this run the agents for me?

No. It produces the two system prompts. You wire them into your own framework (LangGraph, CrewAI, Autogen, or custom code) and supply the model and runtime.

Multi-Agent Coordination Prompt Builder

Multi-agent coordination prompt builder

Multi-agent systems fail in predictable ways: agents talk past each other, the orchestrator never stops, and a single worker error cascades. The fix is a shared protocol — a fixed handoff format, clear role boundaries, defined error handling, and a hard termination condition. This builder writes a matched pair of system prompts (one orchestrator, one worker) that speak the same protocol, so the agents coordinate instead of collide. Paste each prompt into its agent’s system slot in your framework of choice.

How it works

You describe the overall task, list the worker roles, pick a handoff format (JSON or tagged blocks), set a termination condition, and note an error-handling policy. The tool generates two prompts. The orchestrator prompt instructs the model to decompose the task, delegate to named workers using the handoff format, aggregate results, and stop the moment the termination condition is met. The worker prompt instructs each worker to accept a delegated subtask, do exactly that subtask, and reply in the same format — including a structured error reply when it cannot complete. Both run locally until you deploy them.

Tips and examples

Keep workers narrow. A worker that does one thing well is far more reliable than a general one. List specific roles like “researcher,” “coder,” “verifier.”
Make termination unambiguous. “Stop after 5 iterations or when the verifier returns PASS” leaves no room for an endless loop.
Standardize errors. A worker that returns {"status":"error","reason":...} lets the orchestrator retry or reroute instead of crashing.
Match the format to your parser. If your code parses JSON, choose JSON; if you scan for tags, choose tagged blocks — consistency beats elegance.