---
title: "Langfuse Hands-On Workshop: From AI Agent Observability to Evaluation & Continuous Improvement"
date: "2026-11-25"
location: "Dubai (United Arab Emirates)"
category: "Live training"
---

# Langfuse Hands-On Workshop: From AI Agent Observability to Evaluation & Continuous Improvement

**Date**: Wednesday, November 25, 2026<br>
**Time**: 12:00pm GST<br>
**Duration**: 4 hours<br>
**Location**: MediaOne hotel, Dubai<br>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Media 1 Tower - Al Falak St - Exit 32<br>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Dubai Media City - Dubai<br>
**Cost**: FREE<br>
**Format**: Instructor-led training with hands-on labs<br>
**Audience**: AI engineers, developers, data scientists, ML engineers, technical architects<br>

**What You’ll Walk Away With**
- A working agent fully instrumented with Langfuse
- Visibility into LLM calls, tool execution, latency, and errors
- Version-controlled prompts linked to traces
- A reusable evaluation dataset and experiment results
- A repeatable, evidence-based workflow for improving agent quality

**Agenda**:<br>
Start time: 12.00 pm<br>
Lunch and networking: 12.15-1.00pm<br>
Workshop: 1.00pm-4.00pm<br>

- **The AI Engineering Loop** — Why reliable agents are hard, and where observability, prompt management, and evaluation fit in the lifecycle.
- **Meet the Agent** — Set up the sample IT Support Agent and explore its architecture, tool-calling flow, and baseline behaviour.
- **Tracing & Observability** — Instrument the agent with Langfuse. Capture generations, tool calls, latency, and errors; debug from real traces.
- **Prompt Management** — Move prompts into Langfuse, version them, and link changes to agent behaviour.<br>
- **Break** — Networking and Q&A.
- **Monitoring in Production** — Use traces to spot user dissatisfaction, unexpected responses, and quality issues in the wild.
- **Build an Evaluation Dataset** — Turn representative questions and expected behaviours into a reusable test set.
- **Run Experiments** — Evaluate the agent against the dataset, score and compare runs, and prove whether a change improves quality.
- **The Improvement Loop** — Connect tracing, prompts, monitoring, datasets, and experiments into one repeatable workflow.
- **Wrap-Up** — Key takeaways, questions, and applying this to your own applications.

**Before You Arrive**: 
- Bring a laptop with Node.js/npm and Git installed, a Langfuse Cloud account, and your LLM API credentials. 
- Complete the environment setup in advance if possible.

**Materials**: https://langfuse.com/workshop