Multi-Agent Orchestration Prompt Architecture: A Complete Guide

When building a research agent or a multi-agent orchestration system, a well-designed main agent prompt often determines the upper bound of the entire system’s performance on complex tasks. Unlike single-turn conversational applications, the prompt of an orchestration system is essentially a behavior specification. It must not only define who the model is, but also specify how it should think, how work should be divided, how resources should be invoked, and how results should be delivered. This article breaks the prompt into reusable functional modules and examines the design logic and practical considerations behind each module. The goal is to help developers build a clear and extensible agent architecture.

Identity and Goals: The Foundation of the Architecture

Every prompt starts with the definition of identity and goals. This part may seem simple, but it shapes the direction of all subsequent behavior.

A high-quality definition of identity and goals usually includes three layers:

• Role definition: Specify whether the agent is a research assistant, code advisor, or task orchestrator. This directly affects how the agent understands the boundaries of its responsibilities.
• Capability boundary: State what the agent can and cannot do, preventing hallucinated answers in scenarios beyond its capabilities.
• Core goal: Translate user intent into executable and measurable objectives, such as “provide accurate, evidence-based, and clearly structured responses.”

In practice, identity and goals should be as specific as possible. Vague definitions, such as “You are a smart assistant,” provide little constraint. A more explicit definition, such as “You are a rigorous research orchestrator. Any conclusion must include information sources and reasoning paths,” can significantly constrain the model’s behavior.

# Example: Skeleton of identity and goals
You are a rigorous research orchestration agent.
Your goal is to break down complex questions raised by users into executable subtasks,
dispatch appropriate sub-agents to research in parallel, and finally integrate an evidence-based, traceable answer.
You do not fabricate facts; when information is insufficient, you clearly state the limitation and provide a path to obtain it.

Research Process: A Four-Step Thinking Loop

The essence of orchestration is to think before acting. Abstracting the research process into a four-step loop makes the agent’s behavior more predictable and debuggable.

Step 1: Assessment and Decomposition

When receiving user input, the agent’s first action should not be to answer immediately. Instead, it should assess the scale and complexity of the task. Is this a simple query, or a complex topic that requires verification from multiple sources? It should then break the task into several subproblems that can be handled independently. The quality of decomposition directly affects the efficiency of subsequent execution. Decomposition that is too coarse may make subtasks difficult to complete independently, while decomposition that is too fine may create unnecessary orchestration overhead.

Step 2: Query Type Classification

After decomposition, each subtask needs to be classified by type. Common categories include:

• Factual queries: These have clear answers and are suitable for fast retrieval.
• Analytical queries: These require comparison and reasoning across multiple sources.
• Computational queries: These require precise numerical or logical deduction.
• Open-ended exploration: These require divergent research and synthesis.

Query classification is the prerequisite for planning. It determines what resources and tools should be assigned to each task.

Step 3: Planning Based on Classification

Based on the classification, the agent creates an execution plan. It decides which subtasks can run in parallel, which must run sequentially because of dependencies, and which require external tools or internal data. The core of this step is to map classification results to execution strategies. A mature plan should explicitly define each subtask’s input, expected output, dependencies, and priority.

Step 4: Execution

The agent then enters the execution phase. It moves between subtasks according to the plan, dispatches sub-agents or tools to complete the work, and continuously collects intermediate results. The key principle in this phase is closed-loop validation. Each output should be compared with the original goal, and deviations should be corrected promptly rather than allowing execution to continue unchecked.

Sub-Agent Allocation: Rules for Choosing the Number of Agents

When task complexity increases, a single agent may not be able to process all subtasks efficiently. In this case, sub-agents should be introduced for division of labor. The “number rule” means dynamically deciding the number and specialization of sub-agents based on task scale.

Key design points include:

• Prefer quality over quantity: Not all tasks need sub-agents. For simple tasks, direct handling is usually faster. Sub-agents should be introduced only when the task can be clearly decomposed and parallel execution brings clear benefits.
• Match expertise: Assign each sub-agent a clear area of expertise to avoid duplicate work or interference caused by overlapping capabilities.
• Set an upper limit: Too many sub-agents significantly increase coordination costs and context-management pressure. A balance must be struck between benefits and overhead.

Delegation Instructions: Assignment and Allocation

Delegation instructions are the “contract” through which the main agent assigns tasks to sub-agents. A clear delegation instruction should include:

• Task goal: What the sub-agent is supposed to achieve.
• Input materials: The context and constraints needed to complete the task.
• Output specification: The expected return format and level of detail.
• Boundary description: What should not be done, to prevent out-of-scope behavior.

# Example: Delegation instruction template
[Subtask goal] Verify the feasibility of a technical solution
[Inputs] The solution description proposed by the user + relevant background materials
[Output requirements] Conclusion + evidence + confidence level, within 200 words
[Boundaries] Do not introduce unverified external assumptions

The quality of delegation instructions directly determines whether sub-agents can complete their work independently and accurately. The clearer the instruction, the lower the rework rate of sub-agents.

Answer Formatting: Making the Output Usable

Even excellent research is difficult to consume if the final output is disorganized. The answer formatting module defines how the final result should be presented. Common requirements include:

• Conclusion first: Present the core answer first, then expand on the supporting details.
• Structured presentation: Use headings, lists, and code blocks appropriately to organize information.
• Traceability: Mark information sources or reasoning bases so that users can verify conclusions.
• Appropriate length: Adjust the level of detail according to task complexity to avoid being overly verbose or too brief.

Advanced: Guidance for Using Internal Tools

Orchestration systems often need to connect to internal tools or data sources, such as internal knowledge bases, retrieval APIs, and computation services. The prompt should include guidance for using these tools, specifying when to use which tool, how to construct invocation parameters, and how to interpret returned results.

When designing this guidance, follow these principles:

• Align tools with intent: Map each type of query to the most appropriate tool to avoid misuse.
• Provide clear parameter descriptions: Clearly describe each tool’s purpose, inputs, and outputs to reduce invocation errors.
• Define fallback behavior: Specify how to handle cases where a tool call fails or returns an empty result.

Note: The actual tool names, interfaces, and parameters should be determined by the actual system integration. The principles described here are general guidance for tool integration and invocation.

Parallel Tool Calls: The Key Lever for Efficiency

When multiple subtasks are independent, sequential tool calls waste a significant amount of time. Parallel tool calls allow the agent to initiate multiple independent calls in the same turn and then collect the results together. This is an important way to improve the throughput of an orchestration system.

The principle for deciding whether calls can run in parallel is simple: if the inputs of multiple calls do not depend on each other’s outputs, they should be executed in parallel; only calls with dependencies should be executed sequentially. Explicitly stating this principle in the prompt helps the agent more actively identify opportunities for parallelism.

Core Rules: Guardrails for the System

A complete main prompt usually ends with a list of core rules. These rules set the bottom line for the agent’s key behaviors. They often include:

1. Do not fabricate facts or sources. If information is insufficient, state this clearly.
2. Prioritize parallel execution of independent tasks. Use sequential execution only when dependencies exist.
3. Provide clear input and output specifications for every delegation.
4. Present conclusions first, keep the structure clear, and make the basis for conclusions traceable.
5. Apply an explicit fallback strategy when tool calls fail.
6. Match the number of sub-agents to task complexity to avoid waste.
7. Use the original goal as the validation benchmark for all execution, and correct deviations promptly when they are found.

Conclusion

A mature multi-agent orchestration prompt is essentially an executable engineering specification. It integrates identity, thinking process, task decomposition, resource scheduling, tool invocation, and output standards into a closed loop. For developers, understanding these modular design principles is more valuable than memorizing specific wording. When business scenarios change, you can adjust the relevant modules instead of rebuilding the entire system.

Whether building a research agent or automating complex enterprise tasks, such as those based on AI capabilities from Baidu AI Cloud, applying this prompt architecture—assess first, classify, orchestrate, and validate—can significantly improve system stability, scalability, and output quality.