Evaluating AI agents: real lessons in building agentic systems in AWS

The evaluation of AI agents goes far beyond traditional metrics of language models. Amazon Web Services (AWS) engineers who design agentic systems at Amazon Bedrock AgentCore have found that testing an agent’s autonomous behavior — as he plans, executes tools and recovers from errors — requires completely new methodologies. Learning, shared in a new technical article, offers a practical guide to developers facing the same challenge.

Evaluation of AI Agents vs. Traditional Models: Key Differences

Conventional language models are evaluated by response accuracy, coherence and absence of bias. An AI agent needs to be judged for its ability to make chain decisions, interact with external APIs, follow multiple-step plans and correct the route when something goes wrong. At AWS, teams realized that traditional unit tests fail to capture systemic failures — such as infinite loops of tool calls or incorrect choice of tools for a specific task.

AWS AA Agent Assessment Method: Step by Step

The solution developed in Bedrock AgentCore combines three check layers:

Tests of plans generated ** – The agent draws up an action plan for a prompt; the plan is compared to an ideal plan expected.

Tool execution tests – Each call to a tool is validated for correctness of parameters and order of call.

Tests of resilience ** – The agent is subjected to scenarios with slow APIs, authentication failures or partial data to measure its recovery capacity.

This structure allows identifying specific bottlenecks, such as the agent who insists on calling a calculation tool even when the answer is already available in the history. The approach recalls the need for robust simulation environments, something that also appears in other AI implementations, such as partnership between NVIDIA and Foxconn for hospital robotics.

Challenges in the Evaluation of AI Agents: Amazon Engineer Insights

Among the most cited obstacles are long context dependence — agents often lose the thread of the meada in very extensive interactions — and the difficulty in measuring efficiency**: how many unnecessary tool calls does an agent do before completing a task? The team also highlights the importance of training data that include examples of error recovery, something scarce in public datasets. > "Evaluing an agent is like evaluating an intern: it is not enough to know if he corrects the final answer; it is necessary to observe how he thinks, acts and learns from his own mistakes."

Frequently Asked Questions about AI Agent Assessment

Is Amazon Bedrock AgentCore already available in Brazil?

Yes, the service is available in the South region (São Paulo) of AWS, allowing Brazilian companies to implement and evaluate AI agents with low local latency.

What kind of tools can a Bedrock agent use?

AgentCore supports any REST API or Lambda AWS function, as well as native connectors for databases and corporate knowledge systems.

How do I start evaluating my own agent?

AWS recommends creating a set of test tasks representative of actual use, implementing the three levels of verification (plan, execution, resilience) and iterating based on observed failures, using the Bedrock console to monitor logs of each step.