AIエージェントの評価：Strands と AgentCore を用いた本番環境向けブループリントEvaluating AI Agents: A production blueprint with Strands and AgentCore

As organizations move generative AI agents from prototypes into production, the question of how to measure whether those agents actually work has become pressing. A recent post on the AWS Machine Learning Blog addresses this directly, presenting a blueprint for systematically evaluating AI agents built with the Strands agent framework and operated through Amazon Bedrock AgentCore. The guidance matters because agents, unlike single-turn model calls, make multi-step decisions, invoke external tools, and maintain state, which makes their behavior harder to verify and their failures harder to trace.

Strands Agents is an open-source SDK that AWS introduced to simplify building agents using a model-driven approach, in which the language model itself plans and orchestrates steps rather than relying on rigidly coded workflows. AgentCore, part of Amazon Bedrock, provides the surrounding operational layer, including a managed runtime, memory, identity, gateway, and observability services intended to run agents securely at scale. The blueprint combines the two so that teams can not only construct agents but also instrument and assess them throughout their lifecycle, both before deployment and once they are handling live traffic.

A central theme is that evaluating an agent requires more than checking a final answer. The post emphasizes examining the agent's trajectory, meaning the sequence of reasoning steps, tool calls, and intermediate outputs that lead to a result. This allows teams to distinguish an agent that reached a correct conclusion through sound logic from one that arrived at the right output by chance or through flawed intermediate steps. Typical dimensions of evaluation described in this context include task completion, tool selection and invocation accuracy, adherence to instructions, response relevance, and latency or cost efficiency. Assessing these separately helps pinpoint where an agent breaks down.

To operationalize this, the blueprint appears to lean on a mix of evaluation techniques. One common approach is the use of a language model as a judge, in which a separate, often more capable model scores an agent's outputs or reasoning against defined criteria. This is frequently paired with reference-based checks, where outputs are compared to curated ground-truth examples, and with programmatic assertions for cases that have deterministic correct answers. The observability features in AgentCore are positioned as a way to capture the detailed traces needed to feed these evaluations, turning raw agent execution logs into structured data that pipelines can score and monitor over time.

The distinction between pre-deployment and post-deployment evaluation is another key point. Before release, teams can run agents against curated test datasets and scenario suites to establish baseline quality and catch regressions as prompts, models, or tools change. After deployment, continuous evaluation against real interactions helps detect drift, surface edge cases that test sets missed, and confirm that changes in underlying foundation models do not degrade behavior. This mirrors established practices in traditional machine learning operations, where offline validation and online monitoring complement each other.

For readers weighing their options, it is worth noting that agent evaluation is an active and crowded area across the industry. Open-source and commercial tools such as LangSmith, Ragas, DeepEval, and OpenAI's evaluation utilities target overlapping problems, and frameworks like LangGraph, LlamaIndex, and CrewAI offer alternative ways to build the agents themselves. The Strands and AgentCore combination is AWS's integrated take, and its main advantage is likely the tight coupling between building, running, and observing agents within one cloud environment, which can reduce the glue code teams otherwise write to connect these stages.

Some prerequisite concepts help in reading the blueprint. Agents generally depend on tool use, sometimes standardized through interfaces like the Model Context Protocol, and on retrieval-augmented generation when they draw on external knowledge. Understanding these building blocks clarifies why evaluation must span both reasoning quality and the correctness of external interactions.

The broader takeaway is that reliable agent deployment depends as much on measurement infrastructure as on the agent logic itself. The post does not claim to eliminate the inherent unpredictability of language-model-driven systems, but it offers a structured path toward quantifying and monitoring their behavior. For teams already invested in the AWS ecosystem, the blueprint provides a concrete starting point; for others, it illustrates evaluation principles that transfer across frameworks regardless of the underlying vendor.