AWS Introduces aws-ai-ml Skill for Coding Agents
A new agent skill brings optimized generative AI inference and benchmarking capabilities directly to popular coding agents through the Agent Toolkit for AWS.
Radwan AbdulhadiEditor and technology writer
Bringing Inference Optimization to Coding Agents
Engineers increasingly rely on coding assistance tools to accelerate development workflows. To support this shift, Amazon SageMaker AI optimized generative AI inference has introduced the aws-ai-ml skill, available through the Agent Toolkit for AWS .
This specialized skill equips coding agents such as Kiro , Claude Code, and Codex with deep expertise in inference optimization and benchmarking. By installing the skill, existing agents gain the ability to benchmark endpoints, recommend deployment configurations, compare performance runs, and generate executable SageMaker Python SDK v3 code directly on behalf of developers.
Bridging Intent and Infrastructure
Amazon SageMaker AI offers serverful hosting across real-time, batch, and asynchronous modes, supporting on-demand and reserved capacity, heterogeneous instances, virtual private cloud isolation, automatic scaling, and extensive training path integrations. While the platform surface is deep, many engineers arrive with a specific use case, performance target, cost envelope, or model to evaluate rather than a pre-determined instance family.
The agentic experience for Amazon SageMaker AI optimized generative AI inference addresses this challenge. Users can describe their target outcomes, and the coding agent responds by producing executable code that can be reviewed, modified, and executed in local or managed environments. The agent asks targeted clarifying questions, grounds its code in real performance data, and adapts to business constraints similarly to a solutions architect.
Local Setup with the Agent Toolkit
Developers can install the aws-ai-ml skill locally using the Agent Toolkit for AWS . The setup process requires AWS Command Line Interface (AWS CLI) 2.35+ and uv installed on the host machine.
The installation automatically detects available agents, installs necessary skills, and configures the AWS MCP Server . For agents like Kiro and Claude Code, runtime discovery allows agents to search for and load skills on demand without requiring a prior local installation.
Managed Environments in SageMaker Studio
Alternatively, teams preferring managed environments can use the skill within an Amazon SageMaker Studio JupyterLab space. By creating a private space and selecting the pre-configured image, users gain access to a workspace where the agent skill and all necessary dependencies are already provisioned.
Once the environment boots and users authenticate their coding agent via their identity provider, they can open the chat panel, confirm available skills, and begin describing their inference goals in natural language.
Benchmarking and Deployment Support
The agentic experience supports various tasks across the inference optimization lifecycle. If a model is already deployed on an endpoint, developers can ask the agent to benchmark it, generating a Python notebook that executes load tests using the SageMaker Python SDK.
Following a benchmark run, the agent delivers a quantitative performance report detailing throughput, latency metrics, and concurrency limits. These measured results help engineers optimize workloads and identify effective serving configurations for production deployments.
Sources
- AWS Machine Learning BlogNew agent skill: Amazon SageMaker optimized generative AI inference for your coding agent
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