AWS adds SageMaker AI inference skill for coding agents
AWS has released the aws-ai-ml skill for its Agent Toolkit, enabling coding agents like Kiro, Claude Code, and Codex to optimize SageMaker AI inference. The tool benchmarks endpoints, recommends deployment configurations, compares performance runs, and generates SageMaker Python SDK v3 code for deployment. It supports Model Context Protocol (MCP)-compatible agents and integrates with SageMaker…
Key points
- AWS’s **aws-ai-ml skill** lets coding agents optimize **SageMaker AI** inference with benchmarks, recommendations, and Python code generation
- Supports models from **S3**, **Hugging Face Hub**, or **SageMaker JumpStart**; compares configurations with real metrics (throughput, latency)
- Available via **Agent Toolkit for AWS** (local) or **SageMaker Studio JupyterLab** (pre-configured image); requires AWS credentials with SageMaker permissions
The skill automates complex tasks—such as selecting instance types, evaluating models from S3, Hugging Face Hub, or SageMaker JumpStart, and comparing benchmark results—without requiring deep infrastructure knowledge. Users describe their goals in natural language, and the agent provides executable code with performance metrics (throughput, latency, concurrency). AWS emphasizes safety: agents confirm before running benchmarks on live endpoints and clarify missing details. The tool is free to install but requires AWS CLI 2.35+ and proper IAM permissions.
The story so far
3 episodes →- AWS adds SageMaker AI inference skill for coding agentsthis story
New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent
AWS Machine Learning Blog · 5 October 2026
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