{ "source_file": "0930", "source_platform": "个人博客", "author": "Jason Liu", "title_listed": "LLM Observability", "keywords": "可观测性,OpenTelemetry,LLM", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://jxnl.co/writing/2023/04/04/good-llm-observability/", "publish_date": "2023-04", "corpus_stage": "推理部署", "notes": "LLM可观测性实践", "id": "7db671b0598f", "collected_at": "2026-10-02T14:29:30", "title": "Good LLM Observability is just plain observability - Jason Liu", "body_markdown": "Good LLM Observability is just plain observability\n¶\nIn this post, I aim to demystify the concept of LLM observability. I'll illustrate how everyday tools employed in system monitoring and debugging can be effectively harnessed to enhance AI agents. Using Open Telemetry, we'll delve into creating comprehensive telemetry for intricate agent actions, spanning from question answering to autonomous decision-making.\nFor more writing on observability and production AI systems, start with\ndocs/writing/index.md\n.\nWhat is Open Telemetry?\nEssentially, Open Telemetry comprises a suite of APIs, tools, and SDKs that facilitate the creation, collection, and exportation of telemetry data (such as metrics, logs, and traces). This data is crucial for analyzing and understanding the performance and behavior of software applications.\nDemystifying Telemetry in AI\n¶\nThe lack of sufficient observability in many AI agents today hinders their evaluation and optimization in real-world scenarios. By integrating Open Telemetry, we can not only enhance the transparency of these agents through tools like Prometheus, Grafana, and Datadog, but also reincorporate this insight to refine the agents themselves.\nHowever, it's crucial to recognize that what's often marketed as specialized LLM telemetry services are merely superficial dashboards encapsulating basic API interactions. These don't provide the depth required for generating extensive telemetry across the whole stack or the means to meaningfully reintegrate this data into the AI agents.\nApplying Telemetry to AI Agents\n¶\nConsider a conversational agent that formulates SQL queries in response to natural language inquiries, interacting with various data sources through a Router Agent. If issues arise, be it database errors or latency spikes, pinpointing the culprit - whether the LLM, the SQL query, or the database itself - becomes challenging. Current LLM operations rarely offer comprehensive instrumentation of external components, leaving these questions unanswered.\nAdopting standards like Open Telemetry can bridge this gap, offering a holistic view of the agent's actions and their interconnections. This insight is pivotal for enhancing system performance, robustness, and incident detection and resolution.\nThe Potential of Telemetry Data\n¶\nEnvision utilizing telemetry data for model-guided self-evaluation. This approach could revolutionize scalable model evaluation. By analyzing the complete task call graph, we can identify and address inefficiencies - for instance, isolating events leading to high-latency database calls or errors.\nThis data, once fed back into the LLM, could prompt targeted fine-tuning. The LLM might analyze a series of transactions, identifying and ranking documents based on relevance, or suggest corrections in a cycle of calls, thus refining the data for model improvement.\nRedefining Telemetry: The Key to Self-Improvement?\n¶\nTelemetry in the realm of AGI might well be akin to a detailed diary, instrumental for reflection and advancement. With robust telemetry, we gain unprecedented insight into the actions of AI agents, enabling the creation of systems that not only evaluate but also self-optimize complex actions. This synergy of human and computer intelligence, driven by comprehensive telemetry, holds the key to unlocking the full potential of AI systems.\nIn essence, observing LLM systems doesn't necessitate new tools; it requires viewing agent systems through the lens of distributed systems. The distinction lies in the potential exportation of this data for the refinement and distillation of other models.\nA prime example of this process can be found in the\nInstructor documentation\n, where techniques for model distillation and fine-tuning are discussed, demonstrating the power of leveraging telemetry data for model enhancement.\nComments", "metadata": { "title": "Good LLM Observability is just plain observability - Jason Liu", "description": "Explore how to enhance AI agents with LLM observability and Open Telemetry for better performance and optimization.", "keywords": null, "author": "Jason Liu", "og:type": "website", "og:title": "Good LLM Observability is just plain observability - Jason Liu", "og:description": "Explore how to enhance AI agents with LLM observability and Open Telemetry for better performance and optimization.", "og:image": "https://jxnl.co/assets/images/social/writing/2023/04/04/good-llm-observability.png", "og:image:type": "image/png", "og:image:width": "1200", "og:image:height": "630", "og:url": "https://jxnl.co/writing/2023/04/04/good-llm-observability/" }, "paywall": { "listed_paid": false, "detected": false, "marker": "", "public_only": false, "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙" }, "crawl": { "success": true, "status_code": 200, "error": "" }, "code_blocks": [], "images": [], "audio": [], "videos": [], "links": [ { "href": "https://github.com/jxnl/blog/edit/main/docs/writing/posts/llmops.md", "label": "", "type": "a" }, { "href": "https://github.com/jxnl/blog/raw/main/docs/writing/posts/llmops.md", "label": "", "type": "a" }, { "href": "https://instructor-ai.github.io/instructor/", "label": "Instructor documentation", "type": "a" } ], "repair_version": 2, "repaired_at": "2026-10-14T10:52:57Z", "source_url": "https://jxnl.co/writing/2023/04/04/good-llm-observability/", "papers": [], "actual_video_files": [], "body_chars": 3849, "expected_body_images": 0, "discovery_status": "ok", "delivery_status": "ok", "missing_reason": "", "raw_html": "attachments/7db671b0598f/pages/source.html", "local_html": "attachments/7db671b0598f/pages/article.html", "body_base_path": "attachments/7db671b0598f/pages/", "body_markdown_path": "attachments/7db671b0598f/pages/article.md" }