Evaluating AI Agents with Strands and Amazon Bedrock AgentCore

Introduction

Enterprises are increasingly moving AI agents from proof of concept into production, where these agents make tool calls, reason across multiple steps, and hold real conversations with users. Unlike a simple chatbot, an agent’s response depends on choosing the right tool, passing the right parameters, and reasoning correctly across turns. When money, trust, or customer experience is on the line, a fluent-sounding answer is not enough proof that the agent did the right thing.

This blog explains how organizations can build a production-grade evaluation pipeline for AI agents using the Strands Agents SDK and Amazon Bedrock AgentCore. Drawing on a real-world implementation for an online vehicle marketplace, it outlines a two-phase evaluation strategy, a three-layer assessment framework, and a staged deployment pipeline that provides teams with evidence that their agents behave reliably before and after release.

Why Agent Evaluation Is Different?

Traditional large language model evaluation looks at text quality: coherence, factual accuracy, and relevance. Agent evaluation must go further because agents take actions. It selects tools, passes parameters, chains multiple steps together, and must remain coherent across multi-turn conversations. A response can read perfectly while the agent silently called the wrong tool, dropped a filter from an earlier turn, or misread a colloquial description. Because outputs are non-deterministic, one successful test run does not guarantee success on a repeat attempt. Evaluation, therefore, needs to examine task completion, tool-use accuracy, reasoning coherence, consistency across trials, safety, and cost-efficiency, not just fluency.

Approaches to Agent Evaluation

A program combines two complementary phases. Build-time evaluation happens during development and continuous integration, catching issues before an agent reaches users. Production evaluation happens after deployment, sampling live traffic to catch issues that synthetic tests miss. Together, they map to the broader GenAIOps lifecycle, treating quality checks as an ongoing discipline rather than a one-time gate.

Within build-time evaluation, work is organized into three layers, each with its own pass threshold. Layer 1 checks tool usage, verifying that the agent selected the correct tool and passed the correct parameters, using fast, deterministic, code-based graders. Layer 2 checks reasoning by using large-language-model judges, typically Anthropic Claude models accessed through Amazon Bedrock, to assess whether the agent’s decision-making process is logical. Layer 3 checks output quality, again using a model-as-judge approach, to confirm that the final response is helpful, accurate, and actionable. All three layers must pass before a release can proceed, and human review is reserved for calibrating the automated judges on edge cases and safety concerns.

Handling non-determinism is essential here. Because outputs vary between runs, teams measure reliability using two metrics from code-generation research: the probability of succeeding at least once across several attempts, and the more demanding probability of succeeding across several consecutive attempts. For customer-facing agents, the consecutive-success metric matters more, since users expect consistent quality every time.

End-to-End Workflow and Architecture

In production, an agent built on the Strands Agents SDK runs on the Amazon Bedrock AgentCore Runtime, which orchestrates calls across multiple tools and invokes foundation models on Amazon Bedrock for reasoning and embeddings. User queries flow through a web interface to the runtime, trigger the appropriate tool calls, and return a final response. Once deployed, Amazon Bedrock AgentCore Evaluations provides continuous monitoring by integrating with OpenTelemetry instrumentation, sampling a small percentage of production traces, and aggregating metrics into Amazon CloudWatch, where alerts can be configured through Amazon Simple Notification Service.

Two monitoring modes support this architecture. On-demand evaluation lets teams analyze specific interactions pulled from Amazon CloudWatch logs, useful for debugging a reported issue. Online evaluation continuously samples live traffic and scores it against built-in evaluators covering helpfulness, goal success, tool selection, and factual correctness, plus custom evaluators for domain-specific concerns such as data freshness, safety guardrails, and data access scoping.

Challenges in Agent Evaluation

Several challenges make agent evaluation harder than conventional software testing. A precisely worded query may succeed while a more natural, colloquial variant of the same request fails. Multi-turn conversations introduce the risk of context drift, in which an agent loses track of filters or preferences that a user established earlier. Non-deterministic outputs mean a single passing test tells you little about reliability at scale. Safety and compliance also matter, since an autonomous agent might attempt actions with real consequences, and cost efficiency matters too, since an agent requiring many tool calls per task may not be economically sustainable.

Solutions Using AWS Services

AWS addresses these challenges with a combination of open-source tooling and managed services. The strands-agents-evals framework provides output validation, trajectory evaluation, multi-turn conversation simulation, and automated experiment generation, designed to work natively with agents built on the Strands Agents SDK. Amazon Bedrock AgentCore Evaluations extends this into production with built-in and custom evaluators, sampling, and dashboards. Amazon CloudWatch tracks metrics such as task completion rate, tool selection accuracy, helpfulness score, response latency, hallucination rate, and cost per interaction, while Amazon SNS delivers alerts when thresholds are breached. A staged deployment pipeline, moving from build-time evaluation through staging validation, shadow mode, A/B testing, and full rollout, ensures a failing metric blocks a release rather than reaching every user at once.

Conclusion

A fluent response from an AI agent does not confirm that it behaved correctly. Verifying tool selection, reasoning coherence, and consistency across repeated runs is essential before trusting an agent with real users and real outcomes.

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