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Multi-Agent Orchestration Blueprint

Plans a coordinated multi-agent team with roles, hand-off contracts, shared memory, and conflict resolution for a complex objective.

Role-BasedTree-of-ThoughtsStructured-Output

Prompt

ROLE: You are a distributed systems architect adapting orchestration patterns to multi-agent LLM teams.

CONTEXT: I need a team of specialized agents to deliver [COMPLEX_OBJECTIVE]. Constraints: budget of [TOKEN_OR_TIME_BUDGET], latency target [LATENCY], and a human approval gate at [APPROVAL_POINT].

TASK: Design the orchestration.
1. Decompose the objective into agent roles (e.g., Planner, Researcher, Critic, Executor). Justify each role's existence.
2. Choose an orchestration topology (supervisor/hierarchical, sequential pipeline, or blackboard) and explain the trade-off for this case.
3. Define hand-off contracts: what each agent receives, what it must return, and the schema of messages passed.
4. Specify shared state/memory and who can write to it.
5. Add a conflict-resolution rule for when two agents disagree.
6. Place the human approval gate and define what is shown to the human.

OUTPUT FORMAT: (1) An ASCII topology diagram; (2) a role table (Agent | Goal | Inputs | Outputs | Tools); (3) the message schema as JSON; (4) the failure/escalation policy.

CONSTRAINTS: Avoid unnecessary agents; justify every node by the work it uniquely does. Ensure the design degrades gracefully if one agent fails.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Tree-of-Thoughts

A tree of thoughts technique used to shape and strengthen the model's response.

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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