How we evaluate: methodologies and approaches
LLM agents, particularly those built with flexible frameworks like LangChain or LangGraph, are typically composed of different functional parts or skills. An agent’s overall performance isn’t a single monolithic metric; it’s the result of how well it executes these individual capabilities and how effectively they work together. In the following subsection, we’ll delve into these core capabilities that distinguish effective agents, outlining the specific dimensions we should assess to understand where our agent excels and where it might be failing.
Automated evaluation approaches
Automated evaluation methods provide scalable, consistent assessment of agent capabilities, enabling systematic comparison across different versions or implementations. While no single metric can capture all aspects of agent performance, combining complementary approaches allows for comprehensive automated evaluation that complements...
