治理基準觀測 4 min read

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AI Agent Runtime Governance Implementation: Gateway vs Sidecar Pattern

Two production patterns for runtime enforcement in AI agents: gateway-as-control-plane vs sidecar-as-observer. Tradeoffs, measurable metrics, concrete deployment scenarios.

2026年5月8日 4 min read · 入門

Security Orchestration Infrastructure Governance

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

問題背景：誰來強制執行 AI Agent 的運行時規則？

當 AI Agent 從「回答問題的工具」變成「執行任務的實體」，一個關鍵問題浮現：誰來強制執行其運行時規則？ 2026 年的生產環境中，AI Agent 正在跨組織、跨平台自主運作，傳統的「監控」已不足以保證安全與合規。

本篇深入解析兩種實作模式：

閘道器模式（Gateway Pattern）：將所有 Agent → 工具的流量導向中央控制平面
旁觀者模式（Sidecar Pattern）：在 Agent 運行時旁邊部署一個「觀察者/攔截器」容器

兩者都解決了「運行時強制執行」的需求，但架構設計、部署成本、可擴展性與合規成本有顯著差異。

模式一：閘道器模式（Gateway as Control Plane）

架構設計

┌─────────────────────────────────────────────────┐
│                     Application Layer                │
│  (Agent 1, Agent 2, Agent 3 ...)                    │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│              Gateway (Control Plane)               │
│  - Policy Engine: 拦截、驗證、動態調度                 │
│  - Identity Provider: DID/Token 驗證                │
│  - Budget Controller: Token/API quota 管理         │
│  - Evidence Collector: 行為日誌、事件記錄             │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│               External Tools/APIs                  │
│  (Database, Email, CRM, External APIs...)         │
└─────────────────────────────────────────────────┘

實作要點

單一強制執行點
- 所有 Agent → 工具的流量必須經過閘道器
- 閘道器負責：策略驗證、身份鑑別、預算控制、行為審計
- 拒絕任何未通過檢查的請求
動態策略引擎
- 支援即時策略更新（無需重啟 Agent）
- 可根據 Agent 行為、使用者身份、環境上下文動態調整
- 策略格式：JSON Schema + 簽名驗證（Ed25519）
可觀察性深度整合
- 閘道器即為「可觀察性控制平面」
- 自動收集：Prompt、Tool Calls、Intermediate Reasoning、Token 使用量
- 输出 OpenTelemetry trace 與 JSON event logs

測量指標

指標	典型範值	計算方式
閘道器延遲（p99）	< 0.1ms	99% 請求的攔截→回應時間
策略評估吞吐量	> 50k req/s	策略驗證/調度速率
合規攔截率	0.1% ~ 5%	有違規行為被拒絕的比例
行為日誌保留時間	90 天	合規/稽核需求

部署場景

適合： 大型企業、多 Agent 系統、跨平台部署、強合規要求

部署示例（AKS + Gateway）：

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent-gateway
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: gateway
        image: mcr.microsoft.com/agent-governance/gateway:2026.04
        env:
        - name: POLICY_URI
          value: "https://s3.example.com/policies/latest.json"
        - name: OPEN_TELEMETRY_ENABLED
          value: "true"
        ports:
        - containerPort: 8080

成本分析：

閘道器本身：~$500-1500/月（3 replicas + 策略引擎）
策略維護：~$2000-5000/月（策略開發、審計、合規）
運行時收益：避免一次安全事件可能造成的 $100k-1M 損失

模式二：旁觀者模式（Sidecar as Observer）

架構設計

┌─────────────────────────────────────────────────┐
│                     Application Layer                │
│  (Agent 1 + Sidecar 1)                             │
│  (Agent 2 + Sidecar 2)                             │
└─────────────────────────────────────────────────┘
        │                │                │
        ▼                ▼                ▼
┌─────────────────────────────────────────────────┐
│               Sidecar Containers                  │
│  - ToolCallInterceptor: 攔截 Agent → Tool calls    │
│  - PolicyChecker: 驗證工具調用是否合規               │
│  - EvidenceCollector: 收集行為日誌                   │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│               External Tools/APIs                  │
└─────────────────────────────────────────────────┘

實作要點

代理攔截器
- Sidecar 以進程注入方式攔截 Agent 的系統調用
- 使用 eBPF/ptrace 捕獲 tool calls
- 攔截點：systemctl exec、curl、http.get 等工具調用
輕量級策略檢查
- 策略規則較簡單：允許/拒絕/動態限速
- 不需複雜的狀態管理
- 優先「觀察」而非「強制執行」
可選的遠端監控
- Sidecar 可將行為日誌推送到中央可觀察性平台
- 不必依賴中央閘道器，降低單點故障風險

測量指標

指標	典型範值	計算方式
Sidecar 延遲（p99）	0.5 ~ 5ms	攔截→檢查→回應時間
攔截器開銷	1% ~ 5% CPU	相對於 Agent 總負載
日誌量（每天）	10k ~ 500k events	每日 Agent 行為事件數
遠端推送延遲	< 100ms	Sidecar → 可觀察性平台

部署場景

適合： 個別 Agent、小規模部署、快速驗證、DevOps 友好

部署示例（Docker Compose）：

version: '3.8'
services:
  agent:
    image: mycompany/agent:latest
    environment:
    - AGENT_ROLE=customer-support
    - AGENT_SCOPE=hr-only
    volumes:
    - ./sidecar-config.yaml:/etc/sidecar/config.yaml
    depends_on:
    - sidecar
  sidecar:
    image: mcr.microsoft.com/agent-governance/sidecar:2026.04
    command: ["/app/sidecar", "--mode=intercept", "--target=agent"]
    volumes:
    - ./sidecar-config.yaml:/etc/sidecar/config.yaml
    environment:
    - POLICY_ENGINE_URL=http://policy:8080
    - TELEMETRY_ENDPOINT=https://obs.example.com/telemetry

成本分析：

Sidecar 本身：~$50-200/月（單 Agent）
策略維護：~$1000-3000/月（簡化策略）
運行時收益：快速部署、低門檻、易於驗證

對比分析：哪種模式更適合你的情境？

架構層面

维度	閘道器模式	旁觀者模式
強制執行強度	強（單一攔截點）	中（可選攔截）
可擴展性	需水平擴展閘道器	單 Agent 個別部署
跨平台一致性	高（統一控制平面）	中（各 Agent 独立）
單點故障風險	中（閘道器是單點）	低（Sidecar 分散）
合規證明	易（統一日誌）	中（需聚合）

運營層面

维度	閘道器模式	旁觀者模式
部署複雜度	高（需中央控制平面）	低（Sidecar 隨 Agent 部署）
策略管理	複雜（動態策略引擎）	簡單（規則式檢查）
監控集成	原生 OpenTelemetry	需額外推送到平台
故障排查	易（集中日誌）	中（需聚合各 Sidecar）
遷移成本	高（重構 Agent 流量）	低（Sidecar 可獨立）

決策矩陣

選用閘道器模式，當：

需要跨多 Agent 統一控制
遵守嚴格合規要求（GDPR、EU AI Act 等）
已有集中可觀察性平台
預期 Agent 數量 > 10 且跨多團隊

選用旁觀者模式，當：

Agent 數量少（< 5）或快速驗證階段
偏好 DevOps 友好、快速部署
策略規則簡單，無動態調度需求
單 Agent 或小團隊獨立部署

混合模式：漸進式採用

許多組織從 旁觀者模式開始，逐步遷移至 閘道器模式：

階段一：Sidecar 初驗（第 1-3 個月）
- 在個別 Agent 上部署 Sidecar
- 收集行為日誌，識別常見違規模式
- 設計簡化策略規則
階段二：閘道器引入（第 3-6 個月）
- 部署簡化版閘道器（僅攔截+日誌）
- Sidecar 保留為「可選」層
- 閘道器與可觀察性平台整合
階段三：全閘道器遷移（第 6-12 個月）
- 將所有 Agent 流量導向閘道器
- Sidecar 轉為「可觀察者」角色（僅報告）
- 啟用動態策略引擎
階段四：混合運作（第 12+ 個月）
- 核心業務 Agent：閘道器模式
- 實驗/開發 Agent：Sidecar 模式
- 定期評估是否需要全域遷移

測量指標與 ROI 計算

成本 vs 收益

成本項目	閘道器模式	旁觀者模式
開發成本	$5k-15k	$1k-5k
運行成本（月）	$3k-8k	$500-2k
安全事件避免（年）	$100k-1M	$50k-500k

投資回報率（ROI）計算示例

場景： 中型企業，10 個 Agent，預期每季度 1 次安全事件

閘道器模式：
- 成本：$8k/月 × 12 = $96k
- 預期避免事件：$200k × 3 = $600k
- ROI：600k - 96k = $504k（回本約 2 個月）
旁觀者模式：
- 成本：$2k/月 × 12 = $24k
- 預期避免事件：$100k × 3 = $300k
- ROI：300k - 24k = $276k（回本約 1 個月）

實測數據（2026 年 Datadog 調查）

多模型環境：70% 團隊使用 3+ 模型
多提供商：OpenAI 63%，Google 20%，Anthropic 23%
框架採用：LangGraph、LangChain、AutoGen 佔比近 18%
失敗模式：5% LLM 請求失敗，60% 為速率限制

這顯示多模型、多框架、多提供商環境下，單一閘道器更易於管理，而非分散的 Sidecar。

結論

閘道器模式提供統一控制平面，適合大型、合規要求高的生產環境；旁觀者模式輕量、易部署，適合快速驗證或個別 Agent 部署。

關鍵決策點：

Agent 數量、跨團隊/跨平台需求 → 閘道器
快速驗證、策略簡單 → 旁觀者
合規強度 → 閘道器
運營團隊能力 → 旁觀者（易上手）

推薦路徑： 從 Sidecar 開始，驗證策略需求，逐步遷移至閘道器，最終實現混合運作。

參考資料

Microsoft Agent Governance Toolkit（2026.04.02）
Datadog State of AI Engineering 2026
OWASP Agentic AI Top 10（2025.12）
Microsoft Learn - Governance and security for AI agents
Oracle Runtime Governance（blocked site，參考）

Problem background: Who will enforce the runtime rules of the AI Agent?

When the AI Agent changes from a “tool that answers questions” to an “entity that performs tasks”, a key question emerges: Who will enforce its runtime rules? ** In the production environment of 2026, AI Agents are operating autonomously across organizations and platforms, and traditional “monitoring” is no longer enough to ensure security and compliance.

This article provides an in-depth analysis of two implementation modes:

Gateway Pattern: Direct all Agent → Tool traffic to the central control plane
Sidecar Pattern: Deploy an “observer/interceptor” container next to the Agent runtime

Both solve the need for “runtime enforcement”, but there are significant differences in architectural design, deployment costs, scalability and compliance costs.

Mode 1: Gateway mode (Gateway as Control Plane)

Architecture design

┌─────────────────────────────────────────────────┐
│                     Application Layer                │
│  (Agent 1, Agent 2, Agent 3 ...)                    │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│              Gateway (Control Plane)               │
│  - Policy Engine: 拦截、驗證、動態調度                 │
│  - Identity Provider: DID/Token 驗證                │
│  - Budget Controller: Token/API quota 管理         │
│  - Evidence Collector: 行為日誌、事件記錄             │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│               External Tools/APIs                  │
│  (Database, Email, CRM, External APIs...)         │
└─────────────────────────────────────────────────┘

Implementation Points

Single Enforcement Point
- All Agent → tool traffic must pass through the gateway
- The gateway is responsible for: policy verification, identity authentication, budget control, and behavior auditing
- Reject any request that fails the check
Dynamic Strategy Engine
- Supports real-time policy updates (no need to restart Agent)
- Can be dynamically adjusted based on Agent behavior, user identity, and environmental context
- Policy format: JSON Schema + signature verification (Ed25519)
Deep integration of observability
- The gateway is the “observability control plane”
- Automatic collection: Prompt, Tool Calls, Intermediate Reasoning, Token usage
- Output OpenTelemetry trace and JSON event logs

Measurement indicators

Indicators	Typical values	Calculation methods
Gateway delay (p99)	< 0.1ms	99% request interception → response time
Policy evaluation throughput	> 50k req/s	Policy verification/scheduling rate
Compliance interception rate	0.1% ~ 5%	Proportion of rejections with violations
Behavior log retention period	90 days	Compliance/auditing requirements

Deployment scenario

Suitable for: Large enterprises, multi-agent systems, cross-platform deployment, strong compliance requirements

Deployment Example (AKS + Gateway):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent-gateway
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: gateway
        image: mcr.microsoft.com/agent-governance/gateway:2026.04
        env:
        - name: POLICY_URI
          value: "https://s3.example.com/policies/latest.json"
        - name: OPEN_TELEMETRY_ENABLED
          value: "true"
        ports:
        - containerPort: 8080

Cost Analysis:

The gateway itself: ~$500-1500/month (3 replicas + policy engine)
Strategy maintenance: ~$2000-5000/month (strategy development, auditing, compliance)
Runtime benefits: Avoiding a possible $100k-1M loss caused by a security incident

Mode 2: Sidecar as Observer

Architecture design

┌─────────────────────────────────────────────────┐
│                     Application Layer                │
│  (Agent 1 + Sidecar 1)                             │
│  (Agent 2 + Sidecar 2)                             │
└─────────────────────────────────────────────────┘
        │                │                │
        ▼                ▼                ▼
┌─────────────────────────────────────────────────┐
│               Sidecar Containers                  │
│  - ToolCallInterceptor: 攔截 Agent → Tool calls    │
│  - PolicyChecker: 驗證工具調用是否合規               │
│  - EvidenceCollector: 收集行為日誌                   │
└─────────────────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────┐
│               External Tools/APIs                  │
└─────────────────────────────────────────────────┘

Implementation Points

Proxy Interceptor
- Sidecar intercepts Agent’s system calls through process injection
- Use eBPF/ptrace to capture tool calls
- Interception points: systemctl exec, curl, http.get and other tool calls
Lightweight Policy Check
- Policy rules are relatively simple: allow/deny/dynamic speed limit
- No need for complex state management
- Prioritize “observation” rather than “enforcement”
Optional remote monitoring
- Sidecar can push behavioral logs to the central observability platform
- No need to rely on a central gateway, reducing the risk of single points of failure

Measurement indicators

Indicators	Typical values	Calculation methods
Sidecar delay (p99)	0.5 ~ 5ms	Intercept → Check → Response time
Interceptor overhead	1% ~ 5% CPU	Relative to total Agent load
Log volume (daily)	10k ~ 500k events	Number of daily Agent behavior events
Remote Push Latency	< 100ms	Sidecar → Observability Platform

Deployment scenario

Suitable for: Individual Agent, small-scale deployment, quick verification, DevOps friendly

Deployment Example (Docker Compose):

version: '3.8'
services:
  agent:
    image: mycompany/agent:latest
    environment:
    - AGENT_ROLE=customer-support
    - AGENT_SCOPE=hr-only
    volumes:
    - ./sidecar-config.yaml:/etc/sidecar/config.yaml
    depends_on:
    - sidecar
  sidecar:
    image: mcr.microsoft.com/agent-governance/sidecar:2026.04
    command: ["/app/sidecar", "--mode=intercept", "--target=agent"]
    volumes:
    - ./sidecar-config.yaml:/etc/sidecar/config.yaml
    environment:
    - POLICY_ENGINE_URL=http://policy:8080
    - TELEMETRY_ENDPOINT=https://obs.example.com/telemetry

Cost Analysis:

Sidecar itself: ~$50-200/month (single Agent)
Strategy maintenance: ~$1000-3000/month (simplified strategy)
Runtime benefits: rapid deployment, low threshold, easy verification

Comparative analysis: Which mode is more suitable for your situation?

Architecture level

Dimensions	Gateway Mode	Spectator Mode
Enforcement Strength	Strong (single interception point)	Medium (optional interception)
Scalability	Requires horizontal expansion of gateway	Single Agent individual deployment
Cross-platform consistency	High (unified control plane)	Medium (each Agent is independent)
Single point of failure risk	Medium (Gateway is a single point)	Low (Sidecar is decentralized)
Compliance certificate	Easy (unified log)	Medium (requires aggregation)

Operational level

Dimensions	Gateway Mode	Spectator Mode
Deployment complexity	High (requires central control plane)	Low (Sidecar is deployed with Agent)
Policy management	Complex (dynamic policy engine)	Simple (rule-based checking)
Monitoring integration	Native OpenTelemetry	Requires additional push to the platform
Troubleshooting	Easy (centralized logs)	Medium (needs to aggregate sidecars)
Migration cost	High (reconstruct Agent traffic)	Low (Sidecar can be independent)

Decision matrix

Select gateway mode when:

Requires unified control across multiple Agents
Adhere to strict compliance requirements (GDPR, EU AI Act, etc.)
Already have a centralized observability platform
Expected number of Agents > 10 and across multiple teams

Select spectator mode when:

Small number of Agents (< 5) or fast verification phase
Prefer DevOps friendly and fast deployment
The policy rules are simple and there is no need for dynamic scheduling
Independent deployment by single Agent or small team

Hybrid Mode: Progressive Adoption

Many organizations start with spectator mode and gradually move to gateway mode:

Phase 1: Sidecar initial test (1-3 months)
- Deploy Sidecar on individual Agents
- Collect behavioral logs to identify common violation patterns
- Design simplified policy rules
Phase 2: Gateway introduction (months 3-6)
- Deploy a simplified version of the gateway (only interception + log)
- Sidecar remains as an “optional” layer -Gateway integration with observability platform
Phase Three: Full Gateway Migration (Months 6-12)
- Direct all Agent traffic to the gateway
- Sidecar converted to “observable” role (report only)
- Enable dynamic policy engine
Phase Four: Hybrid Operations (Months 12+)
- Core business agent: gateway mode
- Experimental/Development Agent: Sidecar Mode
- Regularly evaluate whether full domain migration is needed

Measurement indicators and ROI calculation

Cost vs Benefit

Cost Items	Gateway Mode	Bystander Mode
Development Cost	$5k-15k	$1k-5k
Operating costs (monthly)	$3k-8k	$500-2k
Security incident avoidance (years)	$100k-1M	$50k-500k

Return on investment (ROI) calculation example

Scenario: Medium-sized enterprise, 10 Agents, expected 1 security incident per quarter

Gateway Mode:
- Cost: $8k/month × 12 = $96k
- Expected avoided events: $200k × 3 = $600k
- ROI: 600k - 96k = $504k (payback takes about 2 months)
Spectator Mode:
- Cost: $2k/month × 12 = $24k
- Expected avoided events: $100k × 3 = $300k
- ROI: 300k - 24k = $276k (payback takes about 1 month)

Actual data (2026 Datadog survey)

Multi-model environment: 70% of teams using 3+ models
Multiple Providers: OpenAI 63%, Google 20%, Anthropic 23%
Framework adoption: LangGraph, LangChain, and AutoGen account for nearly 18%
Failure Mode: 5% of LLM requests failed, 60% rate limited

This shows that in a multi-model, multi-framework, multi-provider environment, a single gateway is easier to manage than dispersed sidecars.

Conclusion

Gateway Mode provides a unified control plane and is suitable for large-scale production environments with high compliance requirements; Bystander Mode is lightweight and easy to deploy, suitable for quick verification or individual Agent deployment.

Key decision points:

Number of Agents, cross-team/cross-platform requirements → Gateway
Quick verification, simple strategy → Bystander
Compliance Strength → Gateway
Operation team capabilities → Bystander (easy to use)

Recommended path: Start with sidecar, verify the policy requirements, gradually migrate to the gateway, and finally achieve hybrid operation.

References

Microsoft Agent Governance Toolkit (2026.04.02)
Datadog State of AI Engineering 2026
OWASP Agentic AI Top 10 (2025.12)
Microsoft Learn - Governance and security for AI agents
Oracle Runtime Governance (blocked site, reference)