New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent
Amazon Web Services Mona Mona
AWS added a skill that lets coding agents tune SageMaker inference for you. It can benchmark endpoints and spit out deployable code instead of guesswork.
Based on reporting by Amazon Web Services, Mona Mona — read the original for the full story.
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AWS is trying to turn a coding agent into a SageMaker inference specialist. The new aws-ai-ml skill, delivered through the Agent Toolkit for AWS, plugs into MCP-compatible agents and gives them enough context to benchmark endpoints, compare runs, recommend deployment settings, and generate SageMaker Python SDK v3 code.
The pitch is simple: engineers usually show up with a goal, not with a clue about which instance family or serving setup to pick. AWS says the skill closes that gap by taking a plain-language request and translating it into executable code, while asking follow-up questions when details are missing. The agent stays visible the whole time, and the output is something you can inspect and run yourself rather than accept in a black box.
The skill works with agents such as Kiro, Claude Code, and Codex, and AWS says it can also be used inside Amazon SageMaker Studio through a pre-configured JupyterLab space. On the local side, setup starts with the Agent Toolkit for AWS, then adding the aws-ai-ml skill. AWS says a working conversation can be reached in about 10 minutes. In Studio, the process involves launching a private JupyterLab space, waiting for it to boot, then authenticating the coding agent.
Once it’s running, the agent covers a few key jobs across the inference workflow. It can benchmark an existing SageMaker endpoint using real traffic, after warning and asking for confirmation first. It can also evaluate models against candidate instances and return ranked deployment options with throughput, latency, time-to-first-token, and concurrency metrics. AWS even shows a comparison example where Qwen3-8B outperformed Qwen3-1.7B on throughput and latency, while the smaller model won on time-to-first-token.
The main point here isn’t that AWS invented benchmarking. It’s that it wants the boring, careful parts of inference tuning to happen inside the same chat window as the code. That’s a sensible move, and a very AWS one: keep the human in charge, keep the outputs measurable, and keep the path to production wrapped in SDK code instead of vibes.
My take — AI-written commentary, not fact-checked reporting
This is the right kind of agent use: narrow, measurable, and tied to real infrastructure instead of free-floating magic. The industry keeps trying to make agents sound omniscient; AWS is at least asking them to do one useful job and show their work. That restraint is refreshing, which is not a sentence often written about agent tooling.
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