---
type: concept
title: "Agent Observability"
description: "Instrumenting agents with tracing, logging, and cost/token metrics so you can see and debug what they actually did."
tags: [observability, tracing, monitoring, cost]
videos: [claude-skills-arent-just-for-claude-heres-how-to-build-them-for-any-agent, the-only-ai-tech-stack-you-need-in-2026, learn-90-of-building-ai-agents-in-30-minutes, claude-codes-real-purpose-its-bigger-than-you-think, how-id-learn-ai-agents-fast-if-i-had-to-start-over-full-roadmap, google-anthropic-and-openais-guides-to-ai-agents-all-in-18-minutes, if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon, openais-brand-new-agents-sdk-crash-course, revealing-my-complete-ai-agent-blueprint, boltdiy-the-future-of-open-source-ai-coding-whats-next, weve-been-building-ai-agents-wrong-until-now, ai-is-too-expensive-if-you-dont-do-this]
created: 2026-07-21
updated: 2026-07-21
---

# Agent Observability

Agent observability is instrumenting an agent so you can see every decision it made: the tool calls, the parameters, the tokens, the latency, and the cost. Cole calls it non-negotiable for production: "the last thing that is core to all of my AI agents is Langfuse. This is for agent observability. And you cannot skip this step." [0:10:58] The stakes are diagnostic blindness: "without a platform like Langfuse you're flying completely blind when your agent is out there running in the wild. When it fails, you'll have no idea why." [0:01:08]

The core capability is **tracing**: "tracing is your way to look into peer into your llms and agents running... seeing where things are going wrong if they do so this is very powerful to help you debug your agents and also monitor them in production." [0:02:23] When a real user reports a problem, tracing is what lets you reconstruct the failure: "we can go in here and see like, okay, where did the agent mess up?... We can look at all the parameters, all the tool calls that it made." [0:23:20] Google, Anthropic, and OpenAI all converge on the same guidance, "specific to agents is have visibility into the agents reasoning so that you can see into what it is deciding to do." [0:13:27]

Observability is also a **cost** discipline. A trace surfaces "how much each request costs, the response times, and all of the conversations that your users are having with your agent" [0:00:36], down to per-prompt token counts: "6,000 Tokens The Prompt was 4,000 which includes what I said and then also everything behind the scenes with the system prompt." [0:17:47] That visibility is what makes cost optimization (like routing most requests to a cheaper model) measurable rather than guesswork.

It is the substrate the rest of reliability stands on: "without something like this, there's no way to set up evaluations, AB test... When you're serious about making your agents reliable, you need a tool like this." [0:11:47] The tooling varies by stack, Langfuse, Logfire (favored with Pydantic AI), Sentry (for Claude Code built into workflows), or the OpenAI Agents SDK's built-in tracing, but the requirement does not: "it is 100% necessary for production." [0:14:26]

## Prerequisites

- [Agent Core Components](./agent-core-components.md)

## Related

- [Agent Evaluation](./agent-evaluation.md)
- [Agent Deployment](./agent-deployment.md)
- [Production-Grade Agents](./production-grade-agents.md)
- [End-to-End Validation](./end-to-end-validation.md)
- [Validation](./validation.md)
- [Model Selection](./model-selection.md)
- [Memory Systems](./memory-systems.md)
- [Agentic Coding](./agentic-coding.md)

## Tools

- [Langfuse](../entities/tools/langfuse.md) - Cole's default observability layer for AI agents.
- [Logfire](../entities/tools/logfire.md) - open-source tracing that pairs with Pydantic AI.
- [Sentry](../entities/tools/sentry.md) - tracing for Claude Code built into custom workflows.
- [OpenAI Agents SDK](../entities/tools/openai-agents-sdk.md) - ships built-in tracing with third-party integrations.
- [LangSmith](../entities/tools/langsmith.md)
- [OpenRouter](../entities/tools/openrouter.md) - an AI router for cost reduction, made measurable by observability.

## Sources

- [The ONLY AI Tech Stack You Need in 2026](../sources/the-only-ai-tech-stack-you-need-in-2026.md) - "[0:10:58] the last thing that is core to all of my AI agents is Langfuse. This is for agent observability. And you cannot skip this step."
- [If You're Serious About Building AI Agents, This is Your Secret Weapon](../sources/if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon.md) - "[0:01:08] without a platform like Langfuse you're flying completely blind when your agent is out there running in the wild. When it fails, you'll have no idea why."
- [OpenAI's BRAND NEW Agents SDK (Crash Course)](../sources/openais-brand-new-agents-sdk-crash-course.md) - "[0:02:23] tracing is your way to look into peer into your llms and agents running checking on all the PRS and the outputs and seeing where things are going wrong if they do"
- [Claude Skills Aren't Just for Claude - Here's How to Build Them for ANY Agent](../sources/claude-skills-arent-just-for-claude-heres-how-to-build-them-for-any-agent.md) - "[0:23:20] we can go in here and see like, okay, where did the agent mess up?... We can look at all the parameters, all the tool calls that it made."
- [How I'd Learn AI Agents FAST if I Had to Start Over (Full Roadmap)](../sources/how-id-learn-ai-agents-fast-if-i-had-to-start-over-full-roadmap.md) - "[0:14:26] it is 100% necessary for production."
- [Google, Anthropic, and OpenAI's Guides to AI Agents ALL in 18 Minutes](../sources/google-anthropic-and-openais-guides-to-ai-agents-all-in-18-minutes.md) - "[0:13:27] specific to agents is have visibility into the agents reasoning so that you can see into what it is deciding to do."
- [Claude Code's Real Purpose (It's Bigger Than You Think)](../sources/claude-codes-real-purpose-its-bigger-than-you-think.md) - "[0:16:14] you need this kind of tracing and that's what Sentry gives us."
- [Learn 90% of Building AI Agents in 30 Minutes](../sources/learn-90-of-building-ai-agents-in-30-minutes.md) - "[0:25:21] we have all of this very rich data around the number of tokens that it used the latency. We can view the tools and also look at the different parameters"
- [Revealing my COMPLETE AI Agent Blueprint](../sources/revealing-my-complete-ai-agent-blueprint.md) - "[0:12:06] if you're using Pydantic AI like I'm going to be using in the series you have Logfire which is again open source absolutely fantastic for monitoring"
- [AI is TOO EXPENSIVE if You Don't Do This](../sources/ai-is-too-expensive-if-you-dont-do-this.md) - "[0:02:19] it is Pennies on the dollar if most of the requests can go to the cheaper model"
- [bolt.diy: The Future of Open Source AI Coding - What's Next?](../sources/boltdiy-the-future-of-open-source-ai-coding-whats-next.md) - "[0:17:47] 6,000 Tokens The Prompt was 4,000 which includes what I said and then also everything behind the scenes with the system prompt"
- [We've Been Building AI Agents WRONG Until Now](../sources/weve-been-building-ai-agents-wrong-until-now.md) - "[0:04:01] have this entire UI to show you what's going on with all your llm calls and Tool calls it is just amazing"
