How to evaluate agents in production

Author: DigitalOcean - Bewertung: 0x - Views:3

Building an AI agent that works on test prompts is easy. Proving it works in production is hard.



In this video, I break down how to properly evaluate AI agents, using a real support triage agent example and explain why traditional software testing approaches don’t work for non-deterministic, LLM-powered systems.



We’ll cover:

👉 Why AI agents fail in production even when they pass demo tests

👉 The core differences between deterministic testing and agent evaluation

👉 How to design evaluation datasets for messy, real-world prompts

👉 How to handle non-determinism with metric-based testing

👉 The shift from binary pass/fail to probabilistic, multi-dimensional evaluation

👉 The most important metrics to consider when building evals in agents.



If you’re building AI agents for production, this video gives you a practical, technical framework from theory to real-world implementation.



Chapters:

00:00 Introduction

00:50 How traditional software testing is different from agentic testing

01:30 How testing for AI agents work

02:25 How to test AI agents

04:26 Core metrics to consider for AI evals

06:32 Conclusion



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