Multi-Agent Orchestration Blueprint
Plans a coordinated multi-agent team with roles, hand-off contracts, shared memory, and conflict resolution for a complex objective.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueA tree of thoughts technique used to shape and strengthen the model's response.
Pins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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