AWS Releases Agent Skill for Generative AI Inference Optimization in SageMaker
AWS has released the aws-ai-ml skill, available via the Agent Toolkit for AWS and SageMaker Studio, enabling coding agents to automate and optimize generative AI inference workflows with SageMaker. Developers can now use natural language prompts to generate Python SDK code for benchmarking, deployment recommendations, and performance comparisons.
What changed?
Amazon Web Services has introduced the aws-ai-ml skill through the Agent Toolkit for AWS, allowing coding agents such as Kiro, Claude Code, and Codex to automate and optimize inference workflows on Amazon SageMaker. The skill is available for installation in the Agent Toolkit or as a pre-configured option in SageMaker Studio. It interfaces with agents using the Model Context Protocol (MCP), enabling them to generate and execute SageMaker Python SDK v3 code for benchmarking endpoints, recommending deployment configurations, and comparing performance metrics.
Why does it matter to an everyday developer?
This skill allows developers to use coding agents to automate repetitive or complex infrastructure tasks related to deploying and optimizing generative AI models on SageMaker. Tasks such as performance benchmarking, instance type evaluation, and deployment comparisons, which normally require detailed infrastructure knowledge and manual coding, can now be initiated with a prompt in natural language. The agent not only generates the required code but also interprets benchmark results, recommends configurations, and highlights changes in key metrics such as throughput and latency. This can reduce trial-and-error, speed up deployment, and lower operational risk by grounding recommendations in actual benchmark data.
What can the developer do now?
With the aws-ai-ml skill installed, developers can:
- Use agents to benchmark running SageMaker endpoints and receive detailed reports (throughput, latency, concurrency) based on real workloads.
- Request recommendations for optimal instance types and deployment configurations, whether their model is custom (on S3), from JumpStart, or on Hugging Face (with license acceptance for gated models).