AWS has made the aws-ai-ml skill available for measuring and optimizing inference
AWS has made the aws-ai-ml skill available through Agent Toolkit for AWS. According to the company, coding agents can measure inference performance in Amazon SageMaker AI, compare runs, recommend configurations and generate executable code.
AWS has made the aws-ai-ml skill available through Agent Toolkit for AWS to optimize inference in Amazon SageMaker AI. According to the company, it adds performance measurement, run comparison and deployment configuration recommendations to coding agents that support Model Context Protocol (MCP), such as Kiro, Claude Code and Codex.
The user describes the required performance, a cost constraint or the model they need to evaluate. According to the company, the agent then creates executable code for SageMaker Python SDK v3 that the user can review, modify and run in their own environment. For an endpoint that is already deployed, it can generate a Python notebook containing a load test.
The skill can be used locally or in Amazon SageMaker Studio JupyterLab. Local installation through Agent Toolkit for AWS requires AWS CLI 2.35+ and uv. Credentials must have permissions for the relevant APIs, including creating endpoints and running measurements or jobs that recommend configurations. A preconfigured image is available in Amazon SageMaker Studio; syncing extensions requires a space with the Private setting.
Why it matters
Developers can obtain code they can review for measuring inference from a natural language prompt. For deployment decisions, what matters is the ability to compare the actual measured performance of individual runs against performance and cost requirements, rather than choosing a configuration based solely on an estimate.
Two audiences, two different impacts
What this means
For individuals
According to the company, a developer using a compatible coding agent can prepare a load test for an existing endpoint using a natural language prompt, then review or modify the generated Python notebook.
For a business
According to the company, teams responsible for production inference get support in choosing a deployment configuration based on measured performance and cost constraints.
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