Lab 24 執行結果日誌 (Logs)#

本檔案記錄了 Lab24_observability_eval.py 背景任務觸發、Task Agent 推理軌跡 JSON 檔案匯出、以及 Evaluator Agent (LLM-as-a-Judge) 健康診斷的完整 Thoughts 思考鏈與 Terminal 輸出成果。

📋 執行指令#

python events/notes/wuulong-notes-blog/content/kb/antigravity-practice/Lab24_observability_eval.py

🖥️ 完整執行輸出日誌#

⏰ [背景任務排程] 觸發模擬的凌晨定時分析與評估任務...

🔵 [Task Agent] 啟動背景同步任務...
   👉 任務指令: 請幫我將排課資料 '張老師: 鋼琴團體課A, 14:00' 寫入資料庫。若寫入失敗請重試。

💾 [工具執行] 嘗試將資料寫入 DB: '張老師: 鋼琴團體課A, 14:00'...

💾 [工具執行] 嘗試將資料寫入 DB: '張老師: 鋼琴團體課A, 14:00'...

💾 [工具執行] 嘗試將資料寫入 DB: '張老師: 鋼琴團體課A, 14:00'...

💾 [工具執行] 嘗試將資料寫入 DB: '張老師: 鋼琴團體課A, 14:00'...
🟢 [Task Agent] 任務結束。推理軌跡已序列化寫入：
   /Users/wuulong/github/bmad-pa/events/notes/wuulong-notes-blog/content/kb/antigravity-practice/logs/logs_task_trajectory.json

⚖️ [Evaluator Agent] 啟動自動化軌跡評估...

🧠 ======= Evaluator Agent 思考與審計軌跡 =======
[Thinking] **Initiating Evaluation Report**
I'm now tasked with evaluating an agent's execution trace, specifically focusing on its operational safety and health. I will adopt the role of an AI System Operation Status Evaluation Expert. My initial focus is on analyzing the provided trace and identifying key indicators for assessment. The report will reflect a comprehensive evaluation.

[Thinking] **Analyzing Loop Behaviors**
I am now focusing on the agent's looping behavior. I'm investigating repeated tool calls and retry counts. Additionally, I will evaluate potential "drift" from the original task. Finally, I will determine the overall system health, specifically SUCCESS or FAILURE, and prepare optimization suggestions, including backoff strategies and retry limits. The analysis will be formatted as a structured Traditional Chinese "Agent Mind and Execution Trace Health Evaluation Report" and saved as an artifact. I've located the artifact directory and will update that information to the user.

[Thinking] **Creating Artifact & Reviewing**
I'm now generating the artifact, "agent_behavior_evaluation_report.md," in the specified directory. I have reviewed the execution trace. I observe a repeated database write attempt, highlighting an error. I've noted three retries, which is critical for system health.

[Thinking] **Diagnosing & Evaluating Status**
I'm now diving deep into loop and retry behavior. The agent had four write_database calls, but with no backoff! That's a critical safety issue. No drift occurred. The task ultimately failed due to a database lock. I will categorize this as a 'FAILED' state, and include detailed system status.

[Thinking] **Formulating Safety Solutions**
I'm now generating "agent_health_report.md" containing crucial optimization recommendations. I'm focusing on safety features: exponential backoff strategies to prevent immediate retries, ensuring agents have a 'sleep' tool, and specifying strict retry limits. I will also incorporate a circuit breaker with alerts to handle persistent database locks effectively.

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📝 ======= Agent 心智與執行軌跡健康評估報告 =======
我已針對您提供的 Task Agent 執行軌跡進行了深入的心智與軌跡評估，並將詳細評估整理成一份結構完整的報告檔案：[agent_health_report.md](file:///Users/wuulong/.gemini/antigravity/brain/48a92f4655688ca820ef6f9fd8f0f905/agent_health_report.md)。

以下是評估的核心亮點摘要：

### 🔍 評估關鍵發現

1. **密集重試 (High-Frequency Looping)**:
   * **狀況**：Agent 總共進行了 **4 次** 呼叫（1 次初始 + 3 次重試）。
   * **風險**：重試之間**缺乏延遲（Back-to-back 緊密重試）**。特別是第二次與第三次嘗試之間沒有任何 `thought` 緩衝與等待工具，這在資料庫鎖定時，容易加劇資料庫的連線隊列堆積，甚至可能造成更嚴重的死鎖與效能雪崩。
2. **零目標偏離 (Zero Drifting)**:
   * **表現**：Agent 的思維非常一致。自始至終聚焦於「排課資料寫入」與「鎖定異常排查」，當確認 3 次重試均失敗後，果斷停止嘗試並輸出結構化的故障診斷給用戶，未發生 any 無關的工具調用。
3. **系統健康診斷**:
   * **任務結果**：❌ **FAILED (任務失敗)**。
   * **自癒能力**：雖任務失敗，但在 LLM 異常應變上表現良好。能自主熔斷，並提供友善的主動診斷給用戶，具備優異的容錯反饋心智。

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### 💡 四大優化建議

* **引進指數退避（Exponential Backoff）與抖動（Jitter）**：避免無間隔的連續重試，隨著重試次數增加延遲時間。
* **配備時間等待（Sleep/Wait）工具**：讓 Agent 能夠主動決策「我決定先等待 3 秒再嘗試」。
* **告警與熔斷機制（Circuit Breaker）**：重試耗盡時主動拋出日誌事件或警報給 DBA/管理員。
* **底層重試上限限制**：在工具 SDK 層級限定最大重試次數，防止 LLM 因幻覺而無限死迴圈。
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