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scalable agents","2026-11-10 10:12:30",false,"---\nname: deploying-scalable-agents\nlicense: MIT\n---\n# 使用 Microsoft Foundry 部署可擴充代理\n\n> [第16課 - 部署可擴充代理](..\u002F..\u002F..\u002F16-deploying-scalable-agents\u002FREADME.md) 的配套技能。\n> 用它來協助學習者將代理從原型轉移到可擴展、可觀察的生產部署。\n> 將每一項建議都根植於課程內容和可運行筆記本；不得自行創造 Foundry API。\n\n\n## 觸發條件\n\n當學習者想要：\n- 將代理部署到 Microsoft Foundry 作為\u003Cstrong>託管代理\u003C\u002Fstrong>，並使其版本化\u002F可觀察。\n- 在\u003Cstrong>用戶端託管、託管代理及代理工作流程\u003C\u002Fstrong>部署模式中進行選擇。\n- 添加\u003Cstrong>模型路由\u003C\u002Fstrong>、\u003Cstrong>回應快取\u003C\u002Fstrong>或\u003Cstrong>有界並行性\u003C\u002Fstrong>來控制延遲與成本。\n- 添加一個\u003Cstrong>評估門檻\u003C\u002Fstrong>，以防止錯誤的代理版本被部署。\n- 添加一個\u003Cstrong>人機介入的核准\u003C\u002Fstrong>步驟，針對高風險操作。\n- 以\u003Cstrong>OpenTelemetry\u003C\u002Fstrong>追蹤為代理進行生產環境的觀察性儀表。\n- 進行\u003Cstrong>冒煙測試\u003C\u002Fstrong>作為快速的部署後門檻。\n\n## 核心心智模型\n\n生產代理主要是在模型之外的運作框架（約佔80%），\n而非模型本身。將每個建議對應到以下關注點之一：\n\n| 關注點 | 原型 → 生產 |\n|---------|------------------------|\n| 託管 | 筆記本 → 版本化託管服務 |\n| 身分識別 | 你的`az login` → 管理身分 + 範圍 RBAC |\n| 狀態 | 記憶體內 → 外部線程\u002F記憶體存儲 |\n| 失敗 | 堆疊追蹤 → 重試、回退、警示 |\n| 成本 | 「幾分錢」→ 被追蹤、路由、快取、預算化 |\n| 品質 | 目視檢查 → 自動評估門檻 |\n| 信任 | 你核准 → 政策 + 人機介入 |\n\n## 部署模式（選擇一種或組合使用）\n\n1. \u003Cstrong>用戶端託管\u003C\u002Fstrong> — 推理迴圈在你的程序中運行。最高控制權；你擁有擴展與狀態管理。\n2. **託管代理（Foundry Agent Service）** — Foundry 託管迴圈，儲存線程，執行 RBAC\u002F內容安全，並於入口網站展示代理。較少控制權，且大幅降低操作複雜度。\n3. \u003Cstrong>代理工作流程\u003C\u002Fstrong> — 多代理\u002F工具組成帶有分支、核准節點及持久檢查點的圖形化流程。\n\n## 生命週期（部署代理的循環）\n\n`create → version → evaluate (gate) → deploy hosted → observe online → collect failures → repeat`。\n**離線評估是一道門檻，而非事後考量** — 版本不會上線，除非通過此門檻。\n在線可觀察性會將真實失敗反饋至離線測試集。\n\n\n## 規模與成本槓桿（優先排序）\n\n1. \u003Cstrong>調整模型大小\u003C\u002Fstrong> — 使用通過評估門檻的最小模型。\n2. \u003Cstrong>依複雜度路由\u003C\u002Fstrong> — 簡單請求用小且快速模型，真正推理用大型模型（DIY 分類器或 Foundry 模型路由器）。\n3. \u003Cstrong>快取\u003C\u002Fstrong> — 對近重複的請求不透過模型呼叫直接回應。\n4. **無狀態設計 + 有界並發** — 外部化狀態；采用指數退避重試。\n\n## 需復現的關鍵模式\n\n從筆記本指向學習者這些\n[`16-python-agent-framework.ipynb`](..\u002F..\u002F..\u002F16-deploying-scalable-agents\u002Fcode_samples\u002F16-python-agent-framework.ipynb)：\n\n- \u003Cstrong>請求處理器\u003C\u002Fstrong>：快取 → 按複雜度路由 → 跟蹤 span → 執行 → 快取。\n- \u003Cstrong>評估門檻\u003C\u002Fstrong>：對離線測試集打分；回傳 `pass_rate >= threshold`，且僅於為真時部署。\n- \u003Cstrong>人工審核\u003C\u002Fstrong>：`@tool(approval_mode=\"always_require\")` 用於大額退款等行動。\n- \u003Cstrong>追蹤\u003C\u002Fstrong>：將每次請求包裹在 `tracer.start_as_current_span(...)` 中，並設置屬性如 `routed.model`、`customer.id`。\n\n## 簡易測試已部署代理\n\n部署後，驗證端點確實有回應（綠燈部署仍可能無聲無息）。\n透過 [AI Smoke Test](https:\u002F\u002Fgithub.com\u002Fmarketplace\u002Factions\u002Fai-smoke-test)\n動作使用 [`.github\u002Fworkflows\u002Fsmoke-test.yml`](..\u002F..\u002F..\u002F..\u002F..\u002F.github\u002Fworkflows\u002Fsmoke-test.yml)\n搭配 [`tests\u002F`](..\u002F..\u002F..\u002Ftests\u002FREADME.md) 中的目錄。執行器會將每個\n提示以 POST 發送至 `POST {project_endpoint}\u002Fagents\u002F{agent_name}\u002Fendpoint\u002Fprotocols\u002Fopenai\u002Fresponses`，\n並對回覆文字斷言。身份需具備 Foundry 專案範圍內的\u003Cstrong>Azure AI User\u003C\u002Fstrong>角色；Token 受眾必須是 `https:\u002F\u002Fai.azure.com\u002F`。\n\n\n\n\n\n\n## 企業控管\n\n- **RBAC**：為每個託管代理配置最少權限的管理身份。\n- **生產中的 MCP**：將所有 MCP 服務器視為不信任邊界 – 固定版本、限制身份範圍、驗證輸出、流控、不外露密鑰。\n\n## 助手的護欄\n\n- 首選課程中採用的標準 `FoundryChatClient(...)` + `provider.as_agent(...)` 模式。\n- 不要承諾尚未驗證的 live-Azure 結果；建議使用簡易測試工作流程確認部署。\n- 將評估與成本建議綁定：評估設定品質底線，路由／快取將成本維持於該底線附近。\n\n---\n\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**免責聲明**：\n此文件已使用 AI 翻譯服務 [Co-op Translator](https:\u002F\u002Fgithub.com\u002FAzure\u002Fco-op-translator) 進行翻譯。雖然我們努力追求準確性，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於關鍵資訊，建議採用專業人工翻譯。我們不對因使用此翻譯所產生的任何誤解或誤譯承擔責任。\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER END -->","# 使用 Microsoft Foundry 部署可擴充代理\n\n> [第16課 - 部署可擴充代理](..\u002F..\u002F..\u002F16-deploying-scalable-agents\u002FREADME.md) 的配套技能。\n> 用它來協助學習者將代理從原型轉移到可擴展、可觀察的生產部署。\n> 將每一項建議都根植於課程內容和可運行筆記本；不得自行創造 Foundry API。\n\n\n## 觸發條件\n\n當學習者想要：\n- 將代理部署到 Microsoft Foundry 作為\u003Cstrong>託管代理\u003C\u002Fstrong>，並使其版本化\u002F可觀察。\n- 在\u003Cstrong>用戶端託管、託管代理及代理工作流程\u003C\u002Fstrong>部署模式中進行選擇。\n- 添加\u003Cstrong>模型路由\u003C\u002Fstrong>、\u003Cstrong>回應快取\u003C\u002Fstrong>或\u003Cstrong>有界並行性\u003C\u002Fstrong>來控制延遲與成本。\n- 添加一個\u003Cstrong>評估門檻\u003C\u002Fstrong>，以防止錯誤的代理版本被部署。\n- 添加一個\u003Cstrong>人機介入的核准\u003C\u002Fstrong>步驟，針對高風險操作。\n- 以\u003Cstrong>OpenTelemetry\u003C\u002Fstrong>追蹤為代理進行生產環境的觀察性儀表。\n- 進行\u003Cstrong>冒煙測試\u003C\u002Fstrong>作為快速的部署後門檻。\n\n## 核心心智模型\n\n生產代理主要是在模型之外的運作框架（約佔80%），\n而非模型本身。將每個建議對應到以下關注點之一：\n\n| 關注點 | 原型 → 生產 |\n|---------|------------------------|\n| 託管 | 筆記本 → 版本化託管服務 |\n| 身分識別 | 你的`az login` → 管理身分 + 範圍 RBAC |\n| 狀態 | 記憶體內 → 外部線程\u002F記憶體存儲 |\n| 失敗 | 堆疊追蹤 → 重試、回退、警示 |\n| 成本 | 「幾分錢」→ 被追蹤、路由、快取、預算化 |\n| 品質 | 目視檢查 → 自動評估門檻 |\n| 信任 | 你核准 → 政策 + 人機介入 |\n\n## 部署模式（選擇一種或組合使用）\n\n1. \u003Cstrong>用戶端託管\u003C\u002Fstrong> — 推理迴圈在你的程序中運行。最高控制權；你擁有擴展與狀態管理。\n2. **託管代理（Foundry Agent Service）** — Foundry 託管迴圈，儲存線程，執行 RBAC\u002F內容安全，並於入口網站展示代理。較少控制權，且大幅降低操作複雜度。\n3. \u003Cstrong>代理工作流程\u003C\u002Fstrong> — 多代理\u002F工具組成帶有分支、核准節點及持久檢查點的圖形化流程。\n\n## 生命週期（部署代理的循環）\n\n`create → version → evaluate (gate) → deploy hosted → observe online → collect failures → repeat`。\n**離線評估是一道門檻，而非事後考量** — 版本不會上線，除非通過此門檻。\n在線可觀察性會將真實失敗反饋至離線測試集。\n\n\n## 規模與成本槓桿（優先排序）\n\n1. \u003Cstrong>調整模型大小\u003C\u002Fstrong> — 使用通過評估門檻的最小模型。\n2. \u003Cstrong>依複雜度路由\u003C\u002Fstrong> — 簡單請求用小且快速模型，真正推理用大型模型（DIY 分類器或 Foundry 模型路由器）。\n3. \u003Cstrong>快取\u003C\u002Fstrong> — 對近重複的請求不透過模型呼叫直接回應。\n4. **無狀態設計 + 有界並發** — 外部化狀態；采用指數退避重試。\n\n## 需復現的關鍵模式\n\n從筆記本指向學習者這些\n[`16-python-agent-framework.ipynb`](..\u002F..\u002F..\u002F16-deploying-scalable-agents\u002Fcode_samples\u002F16-python-agent-framework.ipynb)：\n\n- \u003Cstrong>請求處理器\u003C\u002Fstrong>：快取 → 按複雜度路由 → 跟蹤 span → 執行 → 快取。\n- \u003Cstrong>評估門檻\u003C\u002Fstrong>：對離線測試集打分；回傳 `pass_rate >= threshold`，且僅於為真時部署。\n- \u003Cstrong>人工審核\u003C\u002Fstrong>：`@tool(approval_mode=\"always_require\")` 用於大額退款等行動。\n- \u003Cstrong>追蹤\u003C\u002Fstrong>：將每次請求包裹在 `tracer.start_as_current_span(...)` 中，並設置屬性如 `routed.model`、`customer.id`。\n\n## 簡易測試已部署代理\n\n部署後，驗證端點確實有回應（綠燈部署仍可能無聲無息）。\n透過 [AI Smoke Test](https:\u002F\u002Fgithub.com\u002Fmarketplace\u002Factions\u002Fai-smoke-test)\n動作使用 [`.github\u002Fworkflows\u002Fsmoke-test.yml`](..\u002F..\u002F..\u002F..\u002F..\u002F.github\u002Fworkflows\u002Fsmoke-test.yml)\n搭配 [`tests\u002F`](..\u002F..\u002F..\u002Ftests\u002FREADME.md) 中的目錄。執行器會將每個\n提示以 POST 發送至 `POST {project_endpoint}\u002Fagents\u002F{agent_name}\u002Fendpoint\u002Fprotocols\u002Fopenai\u002Fresponses`，\n並對回覆文字斷言。身份需具備 Foundry 專案範圍內的\u003Cstrong>Azure AI User\u003C\u002Fstrong>角色；Token 受眾必須是 `https:\u002F\u002Fai.azure.com\u002F`。\n\n\n\n\n\n\n## 企業控管\n\n- **RBAC**：為每個託管代理配置最少權限的管理身份。\n- **生產中的 MCP**：將所有 MCP 服務器視為不信任邊界 – 固定版本、限制身份範圍、驗證輸出、流控、不外露密鑰。\n\n## 助手的護欄\n\n- 首選課程中採用的標準 `FoundryChatClient(...)` + `provider.as_agent(...)` 模式。\n- 不要承諾尚未驗證的 live-Azure 結果；建議使用簡易測試工作流程確認部署。\n- 將評估與成本建議綁定：評估設定品質底線，路由／快取將成本維持於該底線附近。\n\n---\n\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**免責聲明**：\n此文件已使用 AI 翻譯服務 [Co-op Translator](https:\u002F\u002Fgithub.com\u002FAzure\u002Fco-op-translator) 進行翻譯。雖然我們努力追求準確性，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於關鍵資訊，建議採用專業人工翻譯。我們不對因使用此翻譯所產生的任何誤解或誤譯承擔責任。\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER END -->",{"name":5,"license":14},"main","f09e94ffe170f8af6d3db7c42987bbe4db227723","translations\u002Fzh-TW\u002F.agents\u002Fskills\u002Fdeploying-scalable-agents",26019,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fai-agents-for-beginners","https:\u002F\u002Faka.ms\u002Fai-agents-beginners","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-07 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