# AI Agent Evaluation

DevFeed: [AI Agent Evaluation](<https://devfeed.tech/articles/ai-agent-evaluation-30448.md>)

Original publisher: [Read original article](<https://booking.ai/ai-agent-evaluation-82e781439d97?source=rss----4d265f07defc---4>)

Author: Antonio Castelli

Published: 2026-01-21T13:11:23Z

Content type: tutorial

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [api](<https://devfeed.tech/tags/api.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [blog-posts](<https://devfeed.tech/tags/blog-posts.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [llm-evaluation](<https://devfeed.tech/tags/llm-evaluation.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [sql](<https://devfeed.tech/tags/sql.md>), [tools](<https://devfeed.tech/tags/tools.md>)

## AI overview

Booking.com's article presents practical guidance for evaluating LLM agents. It explains how agents differ from single LLMs because they can use external tools and iterate through thoughts, actions, and observations, then introduces black-box and glass-box evaluation approaches.

## Source excerpt

AI Agent Evaluation: practical tips at Booking.com Authors: Zeno Belligoli, Antonio Castelli, George Chouliaras This article is the 2nd part of our GenAI evaluation best practices series. You can read the first part, focusing on single LLM evaluation, here. 1. Overview LLM agents are advanced AI systems that leverage large language models to perform complex tasks autonomously. Unlike single LLMs that respond to direct prompts, agents can break down problems, use external tools, and iterate on solutions through a series of thoughts, actions, and observations. This allows them to achieve goals that require more intricate planning, reasoning, and interaction with various environments. Agents can autonomously determine if tool utilization is necessary to fulfill a user's request. For example, they might employ: a calculator for mathematical operations, a flights API to retrieve available flights for a certain destination, or execute a SQL query to fetch information about an hotel reservation from a database. Figure1: Schematic view of an agent. The user might interact with the agent within a multi-turn conversation, asking it to perform various tasks (recommend vacation destinations, ask questions about a hotel etc.). The agent should try to complete the requested tasks abiding by a set of rules (or constraints) provided by the product specification (e.g. do not recommend properties outside the inventory). In performing the tasks the agent has freedom to use a certain set of tools, and it can interact with them via API requests. Tool examples typically range from general (e.g. calculate travel distance) to use case specific functionalities (e.g. retrieving information from a database). While these enhanced capabilities are fundamental to solve tasks which would be difficult (or even impossible) to solve with text generation only, they require a more complex evaluation process compared to evaluating a single "prompt-response" LLM. The evaluation of an agent's performance