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📚 AI Agent Evaluation Series - Part 5 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ←
3. Implementing Automated Evals ←
4. Debugging AI Agents ←
5. Human Review Training Guide ← You are here

Human Review Training: Scoring AI Agents in Langfuse for Better Evaluation

If you're a domain expert who's been asked to review AI agent responses in Langfuse, this guide is for you. You don't need to be a technical expert or an AI specialist—you just need to bring your domain knowledge and judgment to help improve the AI system.

This is training content designed to help you understand exactly what to look for, how to score responses, and how to provide feedback that makes a real difference. Think of this as your handbook for becoming an effective AI reviewer.

What you'll learn:

• The exact 1-5 scoring rubric with real examples
• How to use Langfuse's annotation queue efficiently
• Copy-paste comment templates for common scenarios
• Best practices for consistent, high-quality reviews
• Why your 1-2 minutes of judgment matters more than spending hours

Let's get you ready to make a meaningful impact.

📚 AI Agent Evaluation Series - Part 4 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ←
3. Implementing Automated Evals ←
4. Debugging AI Agents ← You are here
5. Human Review Training Guide →

Debugging AI Agents with Langfuse: Observability & Evals That Actually Work

Building AI agents is exciting. Debugging them when they fail in production? Not so much.

Here's the problem: AI agents don't fail like traditional software. There's no stack trace pointing to line 47. Instead, you get vague responses, hallucinations, or worse—confidently incorrect answers. Your users see the failure, but you have no idea why the agent decided to call the wrong tool, ignore context, or make up facts.

The solution? Observability and evaluation systems built specifically for AI.

In this guide, we'll show you how to use Langfuse to debug AI agents effectively. You'll learn how to trace agent execution, analyze LLM calls, build evaluation datasets, and implement automated checks that catch issues before your users do. Whether you're running simple RAG pipelines or complex multi-agent systems, these techniques will help you ship reliable AI applications.

📚 AI Agent Evaluation Series - Part 3 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ←
3. Implementing Automated Evals ← You are here
4. Debugging AI Agents →
5. Human Review Training Guide →

From Theory to Practice: Automated Evals for AI Agents with Langfuse

We covered why observability and evals matter for AI agents in Part 1 of this series. Now let's get practical: how do you actually implement automated evaluations that run continuously, catch regressions before users do, and give you the confidence to ship faster?

This guide walks through setting up automated evals with Langfuse—from basic quality checks to sophisticated LLM-as-a-judge evaluations. And if you're using Answer Agent, you're already halfway there: Langfuse tracing is built-in, so you can skip the instrumentation headache and jump straight to measuring quality.

📚 AI Agent Evaluation Series - Part 1 of 5

1. Observability & Evals: Why They Matter ← You are here
2. Human-in-the-Loop Evaluation →
3. Implementing Automated Evals →
4. Debugging AI Agents →
5. Human Review Training Guide →

Observability & Evals: Your AI Agent's Survival Tools

You can't improve what you can't see. And when your AI agent gives you a weird answer, you've got two choices: either guess what went wrong, or actually know.

If you're running AI agents in production—or want to—observability and evals are your lifeline. They're not optional tooling. They're the difference between flying blind and actually understanding what's happening inside your agent's reasoning process.

In traditional software development, you're already familiar with observability tools like Datadog or Sentry. You track logs, metrics, and traces to understand system behavior. But with AI agents, you need something deeper. You need to see inside the agent's brain: every model call, every tool use, every intermediate reasoning step.

This article breaks down what observability and evals mean for AI agents, why they matter more than you think, and how tools like Langfuse and AnswerAgent help you move from reactive debugging to true continuous improvement.

📚 AI Agent Evaluation Series - Part 2 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ← You are here
3. Implementing Automated Evals →
4. Debugging AI Agents →
5. Human Review Training Guide →

Human at the Center: Building Reliable AI Agents with Your Feedback

You're not training your replacement—you're scaling your judgment.

Human-in-the-loop (HITL) means experts stay in the driver's seat. The agent proposes; you decide what "good" looks like. Over time, your feedback turns sporadic wins into consistent performance.