Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore
Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent supply chain decisioning system and evaluate it with Amazon Bedrock AgentCore Evaluations using built-in, custom, and explainability evaluators.
Multi-agent systems have emerged as a powerful solution for addressing complex, real-world problems that require reasoning across data sources, tools, and business constraints. However, ensuring that these systems are consistently helpful, accurate, explainable, and adhere to business constraints in production scenarios is a critical challenge.
Amazon Bedrock AgentCore is a platform designed to build, connect, and optimize agents at scale, providing a fully managed capability for assessing agent performance across development and production. Amazon Bedrock AgentCore Evaluations is a key component that addresses this challenge by offering both built-in and custom evaluators to measure agent performance across various quality dimensions, such as helpfulness, task success, and explainability.
This evaluation framework complements traditional evaluation methods that focus solely on model response quality, providing structured, measurable insights into the decision-making process of agentic systems.
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