Evaluate AI Agent Performance with Amazon Bedrock AgentCore

Your AI agent worked well in the demo, impressing stakeholders and efficiently handling test scenarios, but after deployment in the real world, issues arose. Users encountered incorrect tool calls, inconsistent responses, and unexpected failures, creating a gap between expected agent behavior and actual user experience. Agent evaluation presents challenges that traditional software testing cannot address. Since large language models (LLMs) are non-deterministic, the same user query can lead to different tool selections and outputs. This means that to understand your agent's actual behavior, you need to repeatedly test each scenario.

A single test run shows what can happen, but not what typically happens. Without systematic measurement across these variations, teams find themselves trapped in cycles of manual testing and reactive debugging, leading to significant API costs without clear insight into whether changes improve agent performance. This uncertainty makes every prompt modification risky and leaves a fundamental question unanswered: “Is this agent actually better now?” In this post, we introduce Amazon Bedrock AgentCore Evaluations, a fully managed service for assessing AI agent performance throughout the development lifecycle.

We will explain how the service measures agent accuracy across multiple quality dimensions, describe the two evaluation approaches for development and production, and share practical guidance for building agents that can be deployed with confidence. Evaluating agents requires a new approach, as multiple decisions occur in sequence when a user sends a request. The agent determines which tools (if any) to call, executes those calls, and generates a response based on the results. Each step introduces potential failure points.

Defining evaluation criteria, building test datasets that represent real user requests, and choosing scoring methods that can consistently assess quality are crucial. Without this foundational work, the gap between what teams hope their agents will do and what they can prove becomes a real business risk. Bridging this gap requires a continuous evaluation cycle, where teams build test cases, run them against the agent, score the results, analyze failures, and implement improvements.

Amazon Bedrock AgentCore Evaluations was launched at AWS re:Invent 2025 and is now generally available. It manages evaluation models, inference infrastructure, data pipelines, and scaling, allowing teams to focus on improving agent quality rather than building and maintaining evaluation systems. With built-in evaluators, model quotas and inference capacity are fully managed, meaning organizations evaluating many agents are not consuming their own quotas. AgentCore Evaluations examines agent behavior using OpenTelemetry, capturing distributed traces from applications and providing the full context needed for meaningful evaluation.