How to evaluate agents in production

Description

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 🚀 Join the Developer Cloud: https://cloud.digitalocean.com/registrations/new?utm_source=youtube&utm_medium=organic_video&utm_campaign=digitalocean&utm_content=Hqt8EDkHeV4 // STAY CONNECTED 🌏 Follow our blog for the latest updates: https://www.digitalocean.com/blog 🦈 Join our Developer Community on Discord: https://discord.com/invite/digitalocean 🐥 Follow us on X/Twitter: https://x.com/digitalocean 👩‍💻 We're Hiring! See open roles: http://grnh.se/aicoph1