{
  "id": 3707554,
  "title": "Agentic AI Needs a New Kind of Observability, Here's Why",
  "url": "https://urgent.news/2026/08/27/agentic-ai-needs-a-new-kind-of-observability-heres-why",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-27T09:06:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/ashwinidave/agentic-ai-needs-a-new-kind-of-observability-heres-why-47f6"
  },
  "original_language": "en",
  "account": "For over a decade, observability has relied on a straightforward model: a request travels through various services, and any issues can be traced through traces, spotted in metrics, or detailed in logs. However, this model breaks down when dealing with agentic AI. Unlike traditional services, agentic AI doesn't follow a fixed path; it reasons, calls tools, may repeat actions with varied inputs, backtrack, and sometimes do unexpected things. This unpredictability poses challenges for production environments. Traditional tracing assumes a predictable path, but agentic AI's variable execution graphs render this approach less useful. Logs, which previously provided clear insights, now lack the context needed to understand an agent's decision-making process. Metrics, which previously focused solely on technical aspects, now also need to account for qualitative signals like the number of tool calls, the match between the agent's plan and final action, or the frequency of human intervention. To address these challenges, teams are exploring new approaches. First, they're treating the agent's decision trace as a central signal, capturing intermediate reasoning steps and tool choices. Second, they're correlating tool calls similarly to how they correlate microservice calls, using trace IDs to trace the full path. Third, they're tracking task-level outcomes, not just individual call results, to ensure failures are detected despite individual success. Lastly, they're adjusting alerting strategies to accommodate the non-deterministic nature of agentic systems. While the three traditional pillars—logs, metrics, and traces—remain relevant, they now require a fourth layer of reasoning context. However, this new observability framework is still evolving, with industry-wide standards yet to be established. Teams may need to design their own observability solutions, given the current gaps in tooling. Regardless, the shift towards understanding and monitoring agentic AI is an intriguing challenge in the field of observability.",
  "summary": "For the last decade, observability has been built around a fairly simple mental model: a request comes in, it moves through a handful of services, and somewhere in that path, something goes wrong. Traces show you the path. Metrics show you the trend. Logs show you the details. It's worked well enough because the systems we were watching were, fundamentally, deterministic; the same input mostly…",
  "key_points": [
    "Agentic AI's unpredictable nature challenges traditional observability models.",
    "New observability approaches focus on capturing intermediate reasoning steps and tool choices.",
    "Teams are developing custom observability solutions for agentic AI monitoring."
  ],
  "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."
}