📚 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.