Build long-horizon agents with Grok 4.7 on Amazon Bedrock
- Who
- AWS
- What
- demonstrates building and evaluating a tool-calling agent with Grok 4.7 on Amazon Bedrock
- Quote
In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three tasks that require search rather than lookup.
What happened
AWS demonstrates how to build and evaluate a tool-calling agent using Grok 4.7 on Amazon Bedrock, comparing four reasoning effort levels across three synthetic tasks. The agent answered 67 of 72 runs correctly; xhigh achieved 18 of 18, while low achieved 15 of 18 using about an eighth of the output tokens. The guide covers task design, automated grading, production monitoring, and operational controls, noting that these small-scale results are indicative rather than statistically conclusive.
Why it matters
Tasks with provably unique answers and repeated runs provide a reusable way to compare reasoning effort, while separate correctness and token measurements make the trade-off concrete.
The story so far
AWS published a guide to building and evaluating a tool-calling agent with Grok 4.7 on Amazon Bedrock, reporting correct answers in 67 of 72 test runs across three synthetic tasks. The evaluation compared four reasoning effort levels: xhigh answered all 18 runs correctly, while low answered 15 of 18 correctly using about one-eighth as many output tokens as xhigh. AWS cautioned that the small-scale results are indicative rather than statistically conclusive. The tasks required search rather than simple lookup. The guide covers task design, automated grading, production monitoring and operational controls. Running the example requires an AWS account with Amazon Bedrock access and model access enabled for Grok 4.7.
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