When you deploy an agent to production, one of the hardest questions is whether it’s actually working? It’s whether it does what the business needs, at scale.
What happened
The bar isn’t whether it runs. Many teams will tell you they can’t really see this. They spot-check outputs, watch latency, and collect user feedback when they can get it. None of that tells you whether the agent finished the user’s task, stayed on topic across a long session, leaked personally identifiable information (PII), or quietly skipped half the steps it was supposed to follow.
Where the sources line up
Figure 1: Teams usually watch surface signals, but business outcomes are decided by the agent behaviors below the waterline.
Practical impact for readers
Agent evaluation solves this problem. What makes this challenging is that a good score on one metric can hide a failure on another. A support agent can resolve every ticket and still expose personal information in its replies. In this case, the agent is failing although it has a high completion rate. To get evaluation right, measure the behaviors that matter for each agent, at the right level of detail.
Who should pay attention now
This post shows how to do this on Amazon Bedrock AgentCore. We use the AgentCore built-in evaluators together with the DeepEval and AutoEval evaluators directly from AgentCore Evaluations.
What is still unclear
The most common mistake is to start with the metric list and pick the names that sound right. “Helpfulness sounds good, so we use it. ” The result runs and produces numbers, but it gives almost no useful insight. The metric fits its label, not what success means for the agent.
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