Amazon Web Services has introduced the aws-ai-ml agent skill to simplify generative AI inference optimization within Amazon SageMaker AI. Distributed through the Agent Toolkit for AWS, the skill connects to coding assistants that support the Model Context Protocol (MCP), such as Claude Code, Kiro, and Codex. It equips developer agents to run performance tests on SageMaker endpoints, generate executable SageMaker Python SDK v3 code, and recommend deployment setups based on workload targets.
Endpoint Benchmarking and Metric Comparisons
The aws-ai-ml skill allows coding agents to execute load tests on existing SageMaker AI endpoints using the SageMaker Python SDK APIs Workload.synthetic() and start_benchmark(). Rather than relying on static estimates, the tool captures measured metrics, including request and token throughput, latency percentiles, time-to-first-token, inter-token delay, and maximum concurrency limits. Developers can also request automated comparisons between separate benchmark runs to evaluate performance changes across different instances or configuration adjustments.
Multi-Source Hardware Recommendations
Engineers evaluating hardware requirements can supply model details to receive ranked instance recommendations matched to their cost or throughput goals. The skill supports custom models stored in Amazon Simple Storage Service (S3), foundation models from the Amazon SageMaker JumpStart catalog, and open models hosted on the Hugging Face Hub. For gated Hugging Face repositories, the agent prompts developers to accept licensing terms and supply authentication tokens before generating evaluation code.
Environment Setup and Security Boundaries
Developers can integrate the skill locally using the Agent Toolkit for AWS—which requires AWS CLI 2.35+ and uv—or run it in Amazon SageMaker Studio via private JupyterLab spaces with pre-configured environment images. Select assistants such as Kiro and Claude Code can also discover and load the skill dynamically at runtime through the AWS MCP Server. The skill operates using the developer's existing AWS credentials and requests explicit user confirmation before initiating traffic-generating load tests on active endpoints.
AI-generated editorial illustration; not a documentary photograph.
Source: New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent
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