{
  "id": 1653210,
  "title": "AI Observability Explained: What It Is and How It Works",
  "url": "https://urgent.news/2026/08/18/ai-observability-explained-what-it-is-and-how-it-works",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T06:25:25.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/swapnoneel123/ai-observability-explained-what-it-is-and-how-it-works-487"
  },
  "original_language": "en",
  "account": "Traditional monitoring assumes the same input always produces the same output. However, this assumption breaks down when dealing with AI systems. When you send the same prompt to a model twice, you often receive different responses, and neither will throw an error. AI observability addresses this gap by recording every detail of an AI system's behavior for each request, including the prompt, model version, tokens, cost, latency, tool calls, and a judgement of the output's quality. While current monitoring stacks focus on detecting crashes, error rates, and latency, AI observability goes beyond to identify why an AI feature returns incorrect or nonsensical outputs. For instance, an AI model may return a grammatically perfect response that is factually incorrect, or it may fail to execute a tool call when appropriate. These issues are not reflected in standard telemetry, which is designed to monitor infrastructure rather than AI system behavior. To capture these missing details, a new tooling approach is needed. My team uses Bifrost, an open-source AI gateway from Maxim, to demonstrate what this looks like in practice. When tracing a single AI request, the process unfolds as a chain of spans, each representing a step in the request handling. The user's question initiates the root span, followed by spans for vector embedding, vector search, prompt construction, model call, and any subsequent tool calls. Each span records crucial information such as the model version, token count, cost, latency, and quality score. By examining these spans, developers can pinpoint the exact cause of a faulty response, such as poor vector search results or an inadequate prompt. This level of granularity allows for targeted fixes rather than general troubleshooting based on vague error messages. The key elements to capture for every AI request include the full prompt and response, the model version and parameters, token usage and cost, latency broken down into total time and time to first token, tool calls and retries, a unique trace ID for chaining the request, a session or user identifier for reconstructing conversations, and a quality score. While LLM-as-judge models can provide a quantitative measure of response quality, human annotation and implicit user signals also play a role in understanding AI behavior. By implementing AI observability, teams can proactively identify and address issues that traditional monitoring methods overlook, leading to more reliable and accurate AI systems.",
  "summary": "Traditional monitoring rests on one quiet assumption that nobody ever writes down: the same input gives you the same output. Something breaks, you replay the request, you watch it break again, you fix it. Now send the same request to a model twice. You get two different answers, and neither one of them threw an error. AI observability is the practice of recording what happened inside an AI system…",
  "key_points": [
    "AI observability records every detail of AI system behavior for each request.",
    "It captures prompt, model version, tokens, cost, latency, tool calls, and output quality.",
    "AI observability identifies why AI features return incorrect or nonsensical outputs."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}