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title: AI Agent Observability 2026: Debug Production Agents
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# AI Agent Observability 2026: Debug Production Agents

**[Alex CloudStar](https://daily.dev/sources/alexcloudstar)** · [@alexcloudstar](https://daily.dev/alexcloudstar) · 16 min read · 0 upvotes · 0 comments

## Summary

A practical guide to observability for AI agents in production, written from first-hand experience. Covers why traditional logging falls short for agents, how to structure session traces, what to log in each trace, and how to name and tag spans for queryability. Recommends tools like Langfuse, LangSmith, Braintrust, and Helicone. Explains how to run evals both pre-deploy and on live production traffic using LLM-as-judge scoring. Also addresses token and cost observability, debugging non-deterministic failures by diffing traces, and privacy/data handling considerations. Concludes with a minimal but effective observability setup a solo developer can build in a weekend.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.alexcloudstar.com/blog/ai-agent-observability-debugging-production-2026/>

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- [You can’t debug what you can’t see — Observability for AI Agents](https://daily.dev/posts/you-can-t-debug-what-you-can-t-see-observability-for-ai-agents-8yaiqpabv) · CNCF · 1 upvotes · 0 comments

---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#langsmith](https://daily.dev/tags/langsmith), [#observability](https://daily.dev/tags/observability)

[View this post on daily.dev](https://daily.dev/posts/ai-agent-observability-2026-debug-production-agents-sxo8wyw1v)

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