India's Most Futuristic AI Conference Is Back – Bigger, Sharper, Bolder
Most agents demo look great. You type a request, the agent calls a few tools, and a clean answer comes back. Then real users show up with a real data, and things break in ways you never tested. The agent picks the wrong tool, loops on a failling API, leak data it should not see, or makes up a number in a report. Evaluatig an agent is a different job from evaluating a model, because every step between the goal and result can fail. This guide shows you exactly how to evaluate any type of agent from start to finish.
Before you write a single test, write down what success looks like. Skip this and you will end up measuring whatever is easy to measure, which is rearely what the business care about.
The 4th sets your bar. A low-risk agent can ship at 90% task success with human review on the rest. A high-risk agent might need 99%+ on critical paths and a hard block on certain actions regardless of what the models wants to do.
Source: Analytics Vidhya — Read the full article on the original site ↗