---
type: source
title: "If You're Serious About Building AI Agents, This is Your Secret Weapon"
description: "Agent observability via Langfuse is the most underrated, must-have capability for anyone building production AI agents, and it can be wired into any framework (especially Pydantic AI) in minutes."
youtube_id: DpPVEw4dd0w
url: https://www.youtube.com/watch?v=DpPVEw4dd0w
slug: if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon
published: 2025-04-23
duration: "0:27:50"
recency_rank: 97
raw: "../raw/if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon.md"
tags: [observability, langfuse, production, pydantic-ai]
created: 2026-07-21
updated: 2026-07-21
---

# If You're Serious About Building AI Agents, This is Your Secret Weapon

Cole argues that most AI agent development is guesswork, and the single biggest thing separating hobby projects from production-ready agents is observability: the ability to monitor every action an agent takes, what each request costs, response times, and the full record of user conversations. Without it, once an agent is running in the wild you are flying blind when it fails, spending money you can't account for, and unable to iterate on specific responses or bottlenecks. His recommended tool is Langfuse, a fully open-source LLM engineering platform whose core feature is tracing agent executions.

The bulk of the video is a hands-on tour. He walks through Langfuse's interactive live demo (a RAG agent over Langfuse's own docs) to show a real trace: total cost, latency, input/output tokens, user and session IDs, and drill-down into each tool call and the final generation. He then shows three code examples of increasing complexity from a downloadable template: (1) the OpenAI drop-in replacement plus an @observe decorator for instant tracing, (2) a Pydantic AI agent with a Brave MCP web-search tool wired to Langfuse via Logfire and OpenTelemetry using instrument=True, and (3) a full multi-MCP agent with specialized sub-agents where he sets custom span attributes (user ID, session ID, input/output) so the trace is fully populated.

He stresses the value of logging tool-call parameters, tool responses, and errors (he intentionally injects a divide-by-zero to show error traces), and of grouping traces by users and sessions so you can find and debug a specific user's conversation. Langfuse is 100% open-source and self-hostable (bundled into his local AI package, though it depends on Redis, Postgres, ClickHouse, and blob storage), which gives data privacy competitors like LangSmith don't. He closes noting there is no native n8n integration yet, only a community custom-node workaround that requires hardcoding keys.

## Concepts covered

- [Agent Observability](../concepts/agent-observability.md)
- [Agent Deployment](../concepts/agent-deployment.md)
- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Memory Systems](../concepts/memory-systems.md)
- [Agent Teams](../concepts/agent-teams.md)

## Entities

- [Langfuse](../entities/tools/langfuse.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [Logfire](../entities/tools/logfire.md)
- [MCP (Model Context Protocol)](../entities/tools/mcp.md)
- [Archon](../entities/tools/archon.md)
- [OpenAI](../entities/organizations/openai.md)
- [LangSmith](../entities/tools/langsmith.md)
- [n8n](../entities/tools/n8n.md)
- [Brave Search API](../entities/tools/brave-search.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:17]** Introduces Langfuse as a free, open-source LLM engineering platform for agent observability
- **[0:00:36]** Defines observability: monitor every action, cost per request, response times, and all user conversations
- **[0:02:18]** Core feature focus is tracing; broad framework integrations (Pydantic AI, LangChain, LangGraph, CrewAI, Semantic Kernel)
- **[0:02:46]** Langfuse is 100% open-source; managed cloud or fully self-hosted for data privacy
- **[0:03:32]** Self-hosting via Cole's local AI package; depends on Redis, Postgres, ClickHouse, blob storage
- **[0:04:48]** Walks the interactive live demo: a RAG agent over Langfuse docs
- **[0:06:00]** Reads a real trace: total cost, latency, tokens, user/session IDs, tool-call drill-down
- **[0:08:22]** Example 1: OpenAI drop-in replacement + @observe decorator for instant tracing
- **[0:11:36]** Pydantic AI uses Logfire + OpenTelemetry under the hood; set instrument=True
- **[0:12:50]** Example 2: Pydantic AI agent with Brave MCP web-search wired to Langfuse
- **[0:16:46]** Example 3: full multi-MCP agent with specialized sub-agents, built with Archon
- **[0:18:14]** Set custom span attributes: user ID, session ID, input/output values
- **[0:22:35]** Intentionally injects a divide-by-zero to demonstrate error traces
- **[0:24:12]** Sessions and users tabs: filter conversations by user, view per-conversation cost
- **[0:25:26]** No native n8n integration yet; only a custom-node workaround requiring hardcoded keys
- **[0:27:11]** Recommends against deploying any agent to production without an observability platform

## Transcript

[Raw transcript](../raw/if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon.md)
