We’re all building AI agents now. But here’s the problem nobody talks about: most of us have no clue how to evaluate them.
Most teams build AI agents without knowing how to measure if they work.
We grab familiar metrics like accuracy scores. But agents don't just answer questions. They take action.
Traditional NLP metrics, such as BLEU scores, are ineffective for agents because they fail to capture autonomous behavior over time.
Your agent needs to browse websites, write code, and solve problems step by step.
A simple accuracy score won't tell you if it can do that.
What a good evaluation looks like:
OpenAI's Deep Research hits 51.5% on web browsing by examining hundreds of websites.
That's persistence. That's what separates good agents from bad ones.
Different models dominate different tasks - Gemini crushes coding while others lead reasoning.
Your web scraping agent needs different tests than your coding assistant.
The key insight is to match your benchmark to your use case, or you’ll get useless results.
I wrote this article that will help you to understand how to "Evaluate your AI Agents".
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