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Agent Collaboration Topology: Planner-Executor-Verifier-Guard Orchestration Patterns 2026

Frontier AI/agents: Four core roles in multi-agent collaboration patterns, production orchestration workflows, and runtime enforcement tradeoffs

2026年4月11日 6 min read · 入門

Security Orchestration Interface Infrastructure Governance

This article is one route in OpenClaw's external narrative arc.

時間: 2026 年 4 月 11 日 | 閱讀時間: 25 分鐘

前沿信號: 多智能體協作系統正在從「簡單的任務分解」轉向「四層協調架構」。本文對比四種核心協作角色——規劃器、執行者、驗證器、防護者——分析它們的控制模型、交互協議、運行時強制執行策略，並給出生產環境的選擇指南。

導言：協作拓撲不是 prompt engineering

許多人誤以為多智能體協作只是「多個智能體的簡單串聯調用」或「prompt engineering 技巧」。這種理解是危險的錯誤。

真正的多智能體協作是一個有狀態的工具使用型軟件架構，其中規劃器、執行者、驗證器、防護者四個角色在時間上協同工作，解決單個智能體無法可靠處理的複雜問題。

一旦引入四角色協作，你就不再是設計 prompt，而是在設計分層系統。以下四個維度的結構決策——角色定義、控制流、協調機制、交互協議——不再是可選的，而是系統是否工作能的根本。

核心框架：四角色協作拓撲

大多數多智能體分類框架不一致，因為它們混用了不同的分類軸。最可靠的方式是用四個獨立的維度描述任何多智能體系統：

1. 規劃器：決定「做什麼」

核心職責：

將高級目標翻譯為可操作結構
識別子任務、依賴關係、執行順序
生成結構化 I/O schema
明確假設、約束、成功標準

技術特徵：

任務分解深度：單層分解 vs. 多層遞歸分解
依賴圖構建：DAG vs. DAG+條件分支
上下文窗口管理：固定上下文 vs. 動態上下文擴展
規劃預算：固定步數 vs. 動態探索

生產級挑戰：

規劃失敗率：>20% 導致重啟
規劃時間：<30s 端到端
規劃可解釋性：>90% 用戶理解

2. 執行者：決定「如何做」

核心職責：

執行特定子任務
調用工具、API、數據庫
生成輸出、代碼、文本
管理狀態、緩存、回滾

技術特徵：

工具使用模式：單工具 vs. 多工具序列
執行模式：順序 vs. 並發
錯誤處理：重試 vs. 回滾 vs. 報告
狀態管理：短暫狀態 vs. 持久狀態

生產級挑戰：

執行延遲：p95 < 500ms
執行成功率：>99%
執行可觀察性：>95% 輸出可追蹤

3. 驗證器：決定「是否正確」

核心職責：

檢查輸出是否符合期望
驗證約束、規範、格式
檢測邏輯缺口、假設錯誤
生成可操作反饋

技術特徵：

驗證函數類型：Python VFs（數值/類型）、自然語言 VFs（語義）、形式化 VFs（數學）
驗證粒度：輸出級 vs. 步驟級 vs. 任務級
驗證時機：執行後 vs. 規劃時
拒絕策略：重試規劃 vs. 拒絕輸出

生產級挑戰：

驗證延遲：p95 < 100ms
錯誤識別率：>95%
反饋可執行性：>90% 調整方案

4. 防護者：決定「是否安全」

核心職責：

應用安全和合規規則
阻止或修改風險動作
限制工具使用、API 調用
要求升級到人類在迴路

技術特徵：

防護層級：輸入級 vs. 執行級 vs. 輸出級
防護類型：提示詞過濾、工具白名單、API 限流、權限檢查
違規處理：拒絕、警告、報告、升級
防護可解釋性：>90% 規則可追蹤

生產級挑戰：

防護延遲：p95 < 50ms
錯誤阻止率：>98%
人類介入率：<2%

四角色協作模式對比

模式 1：線性協作

架構：規劃器 → 執行者 → 驗證器 → 防護者

特徵：

順序執行：每個角色順序處理輸出
單路徑：一條主流程
簡單狀態：每個角色只有輸入/輸出

優點：

設計簡單
易於理解
調試清晰

缺點：

並行能力弱
錯誤處理有限
擴展性瓶頸

適用場景：簡單任務、原型開發、教育環境

模式 2：循環協作

架構：規劃器 → 執行者 → 驗證器 →（失敗 → 重新規劃）→ 執行者 → 驗證器

特徵：

迭代執行：失敗時重試
動態重規劃：根據錯誤調整計劃
狀態持久化：保存中間狀態

優點：

自動錯誤恢復
逐步優化
適應性強

缺點：

狀態管理複雜
運行時成本高
調試困難

適用場景：複雜任務、科學計算、數據處理

模式 3：並發協作

架構：規劃器 →（分發到多個執行者）→ 驗證器 → 合併結果

特徵：

並發執行：多個執行者同時運行
扇出-扇入：分解 → 並行 → 合併
結果聚合：投票機制、排序機制、置信度加權

優點：

高並行能力
高吞吐量
負載均衡

缺點：

競態條件
通訊開銷
調試複雜

適用場景：大規模數據、並行計算、多模態任務

模式 4：層級協作

架構：規劃器（高層）→ 協調器（中層）→ 執行者（低層）→ 驗證器（低層）→ 防護者（低層）

特徵：

層級控制：高層決策、中層協調、低層執行
職責分離：不同角色在不同層級
多規劃器：戰略規劃 vs. 戰術規劃

優點：

清晰責任
可擴展
職業類比

缺點：

層級延遲
協調瓶頸
管理複雜

適用場景：大型組織、跨領域任務、企業系統

生產環境選擇矩陣

問題診斷

問自己三個問題：

任務複雜度：子任務之間的依賴是固定的還是動態的？
錯誤恢復需求：失敗時是否需要自動重試和重新規劃？
並行需求：是否有大量可並行子任務？

模式選擇矩陣

病症	推薦模式	理由
簡單順序任務	線性協作	設計簡單、易於調試
複雜錯誤恢復	循環協作	自動重試、逐步優化
大規模並行	並發協作	高吞吐量、負載均衡
大型組織	層級協作	清晰責任、職業類比
科學計算	循環協作+並發	迭代優化、並行計算

責任分配矩陣

角色	線性協作	循環協作	並發協作	層級協作
規劃器	高優先級	高優先級	高優先級	高層優先級
執行者	中優先級	中優先級	高優先級	低層優先級
驗證器	中優先級	中優先級	中優先級	低層優先級
防護者	低優先級	低優先級	低優先級	低層優先級

運行時強制執行：防護者實戰

防護層級設計

輸入級防護：

提示詞過濾：檢查輸入是否包含敏感詞、違規模式
工具白名單：只允許指定工具列表
權限檢查：驗證用戶/代理權限

執行級防護：

API 限流：限制每秒請求數
狀態檢查：檢查資源可用性、資金餘額
工具調用限制：限制工具調用頻率

輸出級防護：

輸出過濾：檢查輸出是否包含敏感內容
格式驗證：驗證輸出格式、類型、約束
內容審查：檢查輸出是否違反政策

防護策略

策略 1：基於規則的拒絕

條件：匹配規則模式
動作：立即拒絕輸出
優點：延遲低、可預測
缺點：靈活性低、難以覆蓋邊緣情況

策略 2：基於規則的警告

條件：匹配規則模式
動作：警告 + 允許輸出
優點：靈活性高、可追蹤
缺點：延遲高、潛在風險

策略 3：基於規則的報告

條件：匹配規則模式
動作：記錄、報告、升級
優點：可審計、可調查
缺點：延遲最高

策略 4：人類在迴路

條件：匹配規則模式
動作：請求人類審查
優點：最高安全性
缺點：延遲最高、可擴展性差

防護性能指標

關鍵指標：

防護延遲：p95 < 50ms
錯誤阻止率：>98%
人類介入率：<2%
防護覆蓋率：>95% 規則

監控指標：

防護觸發率：每 1000 請求
防護拒絕率：每 1000 請求
防護警告率：每 1000 請求
人類介入率：每 1000 請求

實戰案例：企業客服 Agent

架構選擇

需求：處理 10,000+ 每日查詢，一致性要求高，快速響應。

選擇：線性協作 + 循環協作混合

架構：

規劃器：分解用戶請求 → 路由到專家
執行者：專家代理執行
驗證器：檢查輸出準確性
防護者：安全過濾

技術實現

規劃器：

def planner(user_request: str) -> Plan:
    # 任務分解
    tasks = decompose(user_request)

    # 路由決策
    routing = route(tasks, user_profile)

    return Plan(tasks=tasks, routing=routing)

執行者：

def executor(task: Task) -> Output:
    # 工具調用
    result = tool.invoke(task.tool, task.params)

    # 狀態保存
    state = save_state(result)

    return Output(result=result, state=state)

驗證器：

def verifier(output: Output) -> ValidationResult:
    # 格式驗證
    if not validate_format(output):
        return ValidationResult(passed=False, reason="Invalid format")

    # 內容驗證
    if not validate_content(output):
        return ValidationResult(passed=False, reason="Content violation")

    return ValidationResult(passed=True)

防護者：

def guard(input: Input, output: Output):
    # 輸入防護
    if violates_policy(input):
        raise SecurityViolation("Input violation")

    # 輸出防護
    if violates_policy(output):
        raise SecurityViolation("Output violation")

    # 工具防護
    if violates_tool_rules(input, output):
        raise SecurityViolation("Tool violation")

關鍵指標

性能指標：

延遲：p95 < 500ms
准確率：>99%
重試率：<3%

防護指標：

防護觸發率：每 1000 請求
防護拒絕率：每 1000 請求
人類介入率：<2%

設計原則與最佳實踐

1. 責任分離原則

原則：每個角色只負責一個維度

實踐：

規劃器：只決定「做什麼」
執行者：只決定「如何做」
驗證器：只決定「是否正確」
防護者：只決定「是否安全」

反模式：

規劃器同時做「做什麼」和「如何做」
執行者同時做「如何做」和「是否正確」
驗證器同時做「是否正確」和「是否安全」

2. 交互協議設計

原則：明確輸入/輸出契約

實踐：

# 規劃器 → 執行者
# 輸入：Plan (tasks, routing, constraints)
# 輸出：Output (result, state)

# 執行者 → 驗證器  
# 輸入：Output
# 輸出：ValidationResult (passed, reason)

# 驗證器 → 防護者
# 輸入：ValidationResult
# 輸出：GuardResult (allowed, action)

3. 錯誤恢復策略

原則：自動恢復 + 人類介入

實踐：

錯誤分級：可恢復（重試）vs. 需人工（升級）
重試策略：固定次數（3次）vs. 動態次數（基於錯誤類型）
時間窗口：固定時間（30s）vs. 動態時間（基於任務大小）

4. 性能優化

原則：盡量減少角色間通信開銷

實踐：

狀態共享：減少中間狀態
批量處理：減少角色調用次數
緩存策略：減少重複驗證

與其他協作模式的對比

vs. 驗證感知規劃

驗證感知規劃：

將「驗證器」與「規劃器」整合
單一系統、單一協調器

四角色協作：

規劃器、執行者、驗證器、防護者分離
多角色協作、多協調器

區別：

驗證感知規劃：單系統、單協調器
四角色協作：多角色、多協調

vs. 管理員-工作者模式

管理員-工作者：

管理員：決定「做什麼」和「如何做」
工作者：只負責「如何做」

四角色協作：

規劃器、執行者、驗證器、防護者分離
明確責任、可觀察性更高

區別：

管理員-工作者：集中控制
四角色協作：分散控制

vs. 層級化協作

層級化協作：

高層、中層、低層三層

四角色協作：

四個獨立角色，可選擇層級化

區別：

層級化協作：三層
四角色協作：四角色，可選擇層級

結論：四角色協作的生產價值

核心要點：

四角色協作不是排他性的：可與其他模式組合使用
責任分離是關鍵：每個角色只負責一個維度
運行時強制執行不可或缺：防護者保護系統安全

生產環境建議：

簡單任務：線性協作
複雜任務：循環協作
大規模並行：並發協作
大型組織：層級協作

下一步行動：

評估當前系統：使用四角色協作診斷
選擇模式：根據需求選擇協作模式
設計角色：明確每個角色責任
實施防護：添加防護層
迭代優化：逐步改進

參考資料

Agent Collaboration Topology: A Unified Guide to Four-Role Orchestration (2026)
Runtime Enforcement for AI Agents: Microsoft Agent Governance Toolkit (2026)
VeriMAP: Verification-Aware Planning Multi-Agent Systems (2026)
Production Agent Architecture: Why 88% Fail to Reach Deployment (2026)

Date: April 11, 2026 | Reading time: 25 minutes

Frontier Signal: Multi-agent collaboration systems are moving from “simple task decomposition” to “four-layer coordination architecture”. This article compares four core collaboration roles—planner, executor, validator, and defender—analyzes their control models, interaction protocols, runtime enforcement strategies, and gives selection guidelines for production environments.

Introduction: Collaboration topology is not prompt engineering

Many people mistakenly believe that multi-agent collaboration is just “a simple series of calls to multiple agents” or “prompt engineering techniques”. This understanding is dangerously wrong.

True multi-agent collaboration is a stateful tool-based software architecture in which the four roles of planner, executor, verifier, and defender work together in time to solve complex problems that cannot be reliably handled by a single agent.

Once four-role collaboration is introduced, you are no longer designing a prompt, but designing a layered system. Structural decisions in the following four dimensions—role definition, control flow, coordination mechanisms, and interaction protocols—are no longer optional, but fundamental to whether the system can work.

Core framework: four-role collaboration topology

Most multi-agent classification frameworks are inconsistent because they mix different classification axes. The most reliable way is to describe any multi-agent system in terms of four independent dimensions:

1. Planner: Decide “what to do”

Core Responsibilities:

Translate high-level goals into actionable structures
Identify subtasks, dependencies, and execution order
Generate structured I/O schema
Clarify assumptions, constraints, and success criteria

Technical Features:

Task decomposition depth: single-level decomposition vs. multi-level recursive decomposition
Dependency graph construction: DAG vs. DAG + conditional branch
Context Window Management: fixed context vs. dynamic context expansion
Planning Budget: fixed number of steps vs. dynamic exploration

Production Level Challenges:

Planning failure rate: >20% leading to restart
Planning time: <30s end-to-end
Planning explainability: >90% user understanding

2. Executor: Decide “how to do it”

Core Responsibilities:

Perform specific subtasks
Call tools, APIs, databases
Generate output, code, text
Manage state, cache, rollback

Technical Features:

Tool Usage Patterns: Single Tool vs. Multi-Tool Sequence
Execution Mode: Sequential vs. Concurrent
Error Handling: Retry vs. Rollback vs. Report
State Management: Transient State vs. Persistent State

Production Level Challenges:

Execution delay: p95 < 500ms
Execution success rate: >99%
Execution observability: >95% output traceability

3. Validator: determine “whether it is correct”

Core Responsibilities:

Check whether the output meets expectations
Verify constraints, specifications, formats
Detect logic gaps and assumptions errors
Generate actionable feedback

Technical Features:

Verification function types: Python VFs (numeric/type), natural language VFs (semantics), formal VFs (mathematics)
Validation granularity: output level vs. step level vs. task level
Time to verify: after execution vs. during planning
Rejection Strategy: Retry Planning vs. Reject Output

Production Level Challenges:

Verification delay: p95 < 100ms
False recognition rate: >95%
Feedback executability: >90% adjustment plan

4. Protector: Decide “whether it is safe”

Core Responsibilities:

Apply security and compliance rules
Block or modify risky actions
Restrict tool usage and API calls
Requires upgrade to Human on the Loop

Technical Features:

Levels of Protection: Input Level vs. Execution Level vs. Output Level
Protection type: Prompt word filtering, tool whitelist, API current limiting, permission check
Violation handling: rejection, warning, report, upgrade
Protection Explainability: >90% rules traceable

Production Level Challenges:

Protection delay: p95 < 50ms
Error blocking rate: >98%
Human intervention rate: <2%

Comparison of four-role collaboration modes

Mode 1: Linear collaboration

Architecture: Planner → Executor → Validator → Defender

Features:

Sequential Execution: Each role processes output sequentially
Single path: one main process
Simple state: only input/output per character

Advantages:

Simple design
easy to understand
Clear debugging

Disadvantages:

Weak parallel capabilities
Limited error handling
Scalability bottleneck

Applicable scenarios: simple tasks, prototype development, educational environment

Mode 2: Loop collaboration

Architecture: Planner → Executor → Validator → (Failure → Replan) → Executor → Validator

Features:

Iterative Execution: Retry on failure
Dynamic Replanning: adjust the plan based on errors
State Persistence: Save intermediate state

Advantages:

Automatic error recovery
Optimize step by step
Adaptable

Disadvantages:

Complex status management
High runtime costs
Difficulty debugging

Applicable scenarios: complex tasks, scientific computing, data processing

Mode 3: Concurrent collaboration

Architecture: Planner → (distribute to multiple executors) → Validator → merge results

Features:

Concurrent Execution: Multiple executors run at the same time
Fan-out-Fan-in: Decompose → Parallel → Merge
Result aggregation: voting mechanism, ranking mechanism, confidence weighting

Advantages:

High parallel capabilities
High throughput
Load balancing

Disadvantages:

Race conditions
Communication overhead
Complicated debugging

Applicable scenarios: large-scale data, parallel computing, multi-modal tasks

Mode 4: Hierarchical collaboration

Architecture: Planner (high level) → Coordinator (middle level) → Executor (low level) → Validator (low level) → Protector (low level)

Features:

Hierarchical control: high-level decision-making, middle-level coordination, and low-level execution
Separation of duties: different roles at different levels
Multiple Planners: Strategic Planning vs. Tactical Planning

Advantages:

Clear responsibilities
Expandable
Career analogy

Disadvantages:

Tier delay
Coordination bottleneck
Complex management

Applicable scenarios: large organizations, cross-domain tasks, enterprise systems

Production environment selection matrix

Problem Diagnosis

Ask yourself three questions:

Task complexity: Are the dependencies between subtasks fixed or dynamic?
Error recovery requirements: Is automatic retry and replanning required in case of failure?
Parallel Requirements: Are there a large number of parallelizable subtasks?

Mode selection matrix

Symptoms	Recommended model	Reasons
Simple sequential tasks	Linear collaboration	Simple design, easy to debug
Complex error recovery	Loop collaboration	Automatic retry, step-by-step optimization
Massive parallelism	Concurrent collaboration	High throughput, load balancing
Large organizations	Hierarchical collaboration	Clear responsibilities, career analogies
Scientific computing	Loop collaboration + concurrency	Iterative optimization, parallel computing

Responsibility Allocation Matrix

Role	Linear collaboration	Circular collaboration	Concurrent collaboration	Hierarchical collaboration
Planner	High Priority	High Priority	High Priority	High Level Priority
Executor	Medium priority	Medium priority	High priority	Low priority
Validator	Medium priority	Medium priority	Medium priority	Low priority
protector	low priority	low priority	low priority	low priority

Runtime Enforcement: Protector in Action

Protection level design

Input level protection:

Prompt word filter: Check whether the input contains sensitive words and violation patterns
Tool Whitelist: Only the specified tool list is allowed
Permission Check: Verify user/agent permissions

Executive Level Protection:

API current limit: limit the number of requests per second
Status Check: Check resource availability, fund balance
Tool call limit: Limit the frequency of tool calls

Output stage protection:

Output filtering: Check whether the output contains sensitive content
Format Verification: Verify output format, type, constraints
Content Moderation: Check output for policy violations

Protection strategy

Strategy 1: Rule-Based Denial

Condition: Match rule pattern
Action: Reject output immediately
Advantages: low latency, predictable
缺点：灵活性低、难以覆盖边缘情况

Strategy 2: Rule-based warnings

Condition: Match rule pattern
Action: warn + allow output
Advantages: high flexibility, traceability
Disadvantages: high delay, potential risks

Strategy 3: Rules-Based Reporting

Condition: Match rule pattern
Actions: record, report, upgrade
Advantages: auditable and investigable
Disadvantages: Highest latency

Strategy 4: Humans in the Loop

Condition: Match rule pattern
Action: Request human review
Advantages: Maximum security
Disadvantages: highest latency, poor scalability

Protection performance indicators

Key Indicators:

Guard Delay: p95 < 50ms
Error Block Rate: >98%
Human Intervention Rate: <2%
Protection Coverage: >95% rule

Monitoring indicators:

Guard Trigger Rate: per 1000 requests
Protection Denial Rate: per 1000 requests
Protection warning rate: per 1000 requests
Human intervention rate: per 1000 requests

Practical Case: Enterprise Customer Service Agent

Architecture selection

Requirements: Process 10,000+ daily queries, with high consistency requirements and fast response.

Choose: Linear Collaboration + Circular Collaboration Hybrid

Architecture:

Planner: Break down user requests → route to experts
Executor: Expert agent execution
Validator: Check output accuracy
Protector: Safe Filtering

Technical implementation

Planner:

def planner(user_request: str) -> Plan:
    # 任務分解
    tasks = decompose(user_request)

    # 路由決策
    routing = route(tasks, user_profile)

    return Plan(tasks=tasks, routing=routing)

Executor:

def executor(task: Task) -> Output:
    # 工具調用
    result = tool.invoke(task.tool, task.params)

    # 狀態保存
    state = save_state(result)

    return Output(result=result, state=state)

validator:

def verifier(output: Output) -> ValidationResult:
    # 格式驗證
    if not validate_format(output):
        return ValidationResult(passed=False, reason="Invalid format")

    # 內容驗證
    if not validate_content(output):
        return ValidationResult(passed=False, reason="Content violation")

    return ValidationResult(passed=True)

Protector:

def guard(input: Input, output: Output):
    # 輸入防護
    if violates_policy(input):
        raise SecurityViolation("Input violation")

    # 輸出防護
    if violates_policy(output):
        raise SecurityViolation("Output violation")

    # 工具防護
    if violates_tool_rules(input, output):
        raise SecurityViolation("Tool violation")

Key indicators

Performance Index:

Latency: p95 < 500ms
Accuracy: >99%
Retry Rate: <3%

Protection indicators:

Guard Trigger Rate: per 1000 requests
Protection Denial Rate: per 1000 requests
Human Intervention Rate: <2%

Design Principles and Best Practices

1. Principle of separation of responsibilities

Principle: Each role is only responsible for one dimension

Practice:

Planner: just decide “what to do”
Executor: only decides “how”
Validator: only determines “whether it is correct”
Protector: only decides “whether it is safe”

Anti-Pattern:

Planner does both “what to do” and “how to do”
The executor does both “how to do it” and “whether it is correct”
The validator does “whether it is correct” and “whether it is safe” at the same time

2. Interaction protocol design

Principle: Clear input/output contracts

Practice:

# 規劃器 → 執行者
# 輸入：Plan (tasks, routing, constraints)
# 輸出：Output (result, state)

# 執行者 → 驗證器  
# 輸入：Output
# 輸出：ValidationResult (passed, reason)

# 驗證器 → 防護者
# 輸入：ValidationResult
# 輸出：GuardResult (allowed, action)

3. Error recovery strategy

Principle: Automatic recovery + human intervention

Practice:

Error classification: recoverable (retry) vs. manual (upgrade) required
Retry strategy: fixed number (3 times) vs. dynamic number (based on error type)
Time window: fixed time (30s) vs. dynamic time (based on task size)

4. Performance optimization

Principle: Minimize communication overhead between roles

Practice:

State sharing: reduce intermediate states
Batch processing: reduce the number of role calls
Caching strategy: reduce repeated verification

Comparison with other collaboration models

vs. Verification Awareness Planning

Verify awareness planning:

Integrate “validator” and “planner”
Single system, single coordinator

Four-role collaboration:

Separation of planners, executors, validators, and defenders
Multi-role collaboration, multi-coordinator

Difference:

Verification awareness planning: single system, single coordinator
Four-role collaboration: multiple roles, multiple coordination

vs. Administrator-Worker Mode

Admin-Worker:

Administrator: Decide “what to do” and “how to do it”
Worker: only responsible for “how to do it”

Four-role collaboration:

Separation of planners, executors, validators, and defenders
Clear responsibilities and higher observability

Difference:

Admin-worker: centralized control
Four-character collaboration: decentralized control

vs. hierarchical collaboration

Hierarchical collaboration:

Three floors: high-rise, mid-rise and low-rise

Four-role collaboration:

Four independent characters with optional hierarchies

Difference:

Hierarchical collaboration: three layers
Four-character collaboration: four characters, selectable levels

Conclusion: The production value of four-role collaboration

Core Points:

Four-character collaboration is not exclusive: can be combined with other modes
Separation of responsibilities is key: Each role is only responsible for one dimension
Runtime enforcement is essential: Guardians keep systems safe

Production Environment Recommendations:

Simple Task: linear collaboration
Complex tasks: Loop collaboration
Massively Parallel: concurrent collaboration
Large Organizations: Hierarchical collaboration

Next steps:

Assess Current System: Using Four Role Collaborative Diagnostics
Select mode: Select the collaboration mode according to your needs
Design roles: Clarify the responsibilities of each role
Implement Protection: Add a layer of protection
Iterative Optimization: Gradual improvements

References

Agent Collaboration Topology: A Unified Guide to Four-Role Orchestration (2026)
Runtime Enforcement for AI Agents: Microsoft Agent Governance Toolkit (2026)
VeriMAP: Verification-Aware Planning Multi-Agent Systems (2026)
Production Agent Architecture: Why 88% Fail to Reach Deployment (2026)