Evaluating Agentic LLM Applications: Metrics and Testing Strategies

Author(s): Mehdi Zare

Originally published on Towards AI.

Agentic LLM applications — think ChatGPT-like agents that plan steps, use tools, and make autonomous decisions — are powerful but notoriously hard to evaluate. Unlike a single-step LLM response, an agentic LLM might perform a sequence of actions (e.g. web searches, calculations) before producing an answer. This multi-step, non-deterministic behavior poses unique challenges for measurement and testing​. In this post, we’ll explore why such agents are difficult to evaluate, define core evaluation goals (from correctness to alignment), and discuss key metrics and strategies. We’ll also walk through a simple example using Python and LangChain — building a toy agent, instrumenting it with logging (via LangSmith), and writing basic evaluation functions. Finally, we’ll point to resources and next steps (like regression testing and trace analysis) to keep improving your LLM agent’s performance.

Variability and Non-Determinism: Agentic LLMs can produce different outcomes even with the same input. They rely on stochastic language model decisions and often interact with external tools or environments. Two runs of the same agent prompt may result in different reasoning paths or outputs. Traditional evaluation methods (like checking a single answer) may only capture a “snapshot” of performance​. For example, an agent might succeed in one out of five… Read the full blog for free on Medium.

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Published via Towards AI