AWS described how to build and evaluate an agent with Grok 4.7
AWS demonstrated how to build an agent with Grok 4.7 on Amazon Bedrock. According to its test, the agent succeeded in 67 of 72 runs; the low setting consumed approximately one-eighth as many output tokens as xhigh, but had a lower success rate.
AWS published a procedure for building and evaluating an agent that uses Grok 4.7 on Amazon Bedrock and calls tools. The test environment included 20 services exposed through seven tools. The agent tackled diagnosing an outage affecting two services based on alerts, scheduling maintenance under simultaneous constraints, and selecting an optimal set of migrations within a budget. Each task had a single correct answer verified by brute force.
AWS ran each of the three tasks six times at each of four effort settings (the amount of reasoning the model performs before each response): low, medium, high and xhigh. According to its measurements, the agent answered correctly in 67 of 72 runs. The xhigh setting achieved 18 correct answers out of 18, while low succeeded in 15 of 18 with approximately one-eighth of the output token consumption of xhigh. These are results from this test, rather than independent confirmation of performance on other tasks. The article compares token counts, rather than monetary costs.
Grok 4.7 is available on Amazon Bedrock and supports tool calling and adjustable effort. When using the Converse API, this setting is passed in additionalModelRequestFields. Tokens consumed by reasoning count toward the output token limit; if the limit is exhausted during reasoning, the response contains no final text, according to the article. The test used a limit of 32 768 tokens. Details are available in the source article.
Why it matters
The procedure gives development teams a concrete template for evaluating agents on tasks that require devising and verifying a solution. Definitively verified answers allow teams to compare accuracy with token consumption and choose settings according to the requirements of their own task. The results of the three test tasks do not, on their own, establish reliability during real outages or migrations.
Two audiences, two different impacts
What this means
For individuals
When implementing an agent, developers must account for reasoning consuming the output token limit and potentially exhausting it before the final response is generated.
For a business
The choice of effort level matters for an agent’s operating budget: measurements by AWS show differences in both token consumption and accuracy. A company therefore needs to verify this relationship on its own tasks before using it to configure production operations.
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