Évaluer les agents IA pour la production : un guide pratique de Strands Evals

Moving AI agents from prototypes to production surfaces a challenge that traditional testing is unable to address. Agents are flexible, adaptive, and context-aware by design, but the same qualities that make them powerful also make them difficult to evaluate systematically. Traditional software testing relies on deterministic outputs: same input, same expected output, every time. AI agents break this assumption. They generate natural language, make context-dependent decisions, and produce varied outputs even from identical inputs. How do you systematically evaluate something that is not deterministic? In this post, we show how to evaluate AI agents systematically using Strands Evals . We walk through the core concepts, built-in evaluators, multi-turn simulation capabilities and practical approaches and patterns for integration. Strands Evals provides a structured framework for evaluating AI agents built with the Strands Agents SDK, offering evaluators, simulation tools, and reporting capabilities. Whether you need to verify that your agent uses the right tools, produces helpful responses, or guides users toward their goals, the framework provides infrastructure to measure and track these qualities systematically. Why evaluating AI agents is different When you ask an agent “What is the weather like in Tokyo?”, many valid responses exist, and no single answer is definitively correct. The agent might report temperature in Celsius or Fahrenheit, include humidity and wind, or only focus on temperature. These variations could be correct and helpful, which is exactly why traditional assertion-based testing falls short. Beyond text generation, agents also take action. A well-designed agent calls tools, retrieves information, and makes decisions throughout a conversation. Evaluating the final response alone misses whether the agent took appropriate steps to reach that response. Even correct responses can fall short. A response might be factually accurate but unhelpful, or helpful but unfaithful to source materials. No single metric captures these different quality dimensions. Conversations add another layer of complexity because they unfold over time. In multi-turn interactions, earlier responses affect later ones. An agent might handle individual queries well but fail to maintain a coherent context across a conversation. Testing single turns in isolation misses these interaction patterns. These characteristics demand evaluation that requires judgment rather than keyword comparison. Large language model (LLM)-based evaluation addresses this need. By using language models as evaluators, we can assess qualities like helpfulness, coherence, and faithfulness that resist mechanical checking. Strands Evals embraces this flexibility while still offering rigorous, repeatable quality assessments. Core concepts of Strands Evals Strands Evals follows a pattern that should feel familiar to anyone who has written unit tests but adapts it for the judgment-based evaluation that AI agents require. The framework introduces three foundational concepts that work together: Cases, Experiments, and Evaluators . Figure: High-Level Architecture A Case represents a single test scenario. It contains the input that you want to test, perhaps a user’s query like “What is the weather in Paris?”, along with optional expected outputs, expected tool sequences known as trajectories, and metadata. Cases are the atomic unit of evaluation. Each one defines one scenario that you want your agent to handle correctly. from strands_evals import Case case = Case( name="Weather Query", input="What is the weather like in Tokyo?", expected_output="Should include temperature and conditions", expected_trajectory=["weather_api"] ) An Experiment bundles multiple Cases together with one or more evaluators. Think of it as a test suite in traditional testing. The Experiment orchestrates the evaluation process. It takes each Case, runs your agent on it, and applies the configured evaluators to score the results. Evaluators are the judges. They examine what your agent produced (the actual output and trajectory) and compare it against what was expected. Unlike simple assertion checks, evaluators in Strands Evals are primarily LLM-based. They use language models to make nuanced judgments about quality, relevance, helpfulness, and other qualities that cannot be reduced to string comparison. Separating these concerns helps keep the framework flexible. You can define what to test with Cases, how to test it with evaluators, and the framework handles orchestration and reporting through Experiments. Each piece can be configured independently so that you can build evaluation suites that are tailored to your specific needs. The task function: connecting agents to evaluation Cases define your scenarios, and evaluators provide judgment. But how does your agent actually connect to this evaluation system? That is where the Task Function comes in. A Task Function is a callable that