Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting there is complex, even for experienced teams. It requires mastering fine-tuning techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning Verifiable Rewards (RLVR), navigating fragmented APIs and model-specific data formats, designing rigorous evaluations, and managing months-long experiment cycles.

Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. Purpose-built agent skills deliver specialized expertise on fine-tuning applied to your specific use case, data transformation to required formats, quality evaluation using LLM-as-a-Judge metrics, and flexible deployment to Amazon Bedrock or SageMaker AI endpoints. Agent skills for model customization not only boost productivity but also decrease token usage. All generated code is fully editable, producing reusable artifacts that integrate seamlessly into existing workflows.

What makes this experience truly powerful is agent Skills for model customization. They are pre-built, modular instruction sets that encode deep AWS and data science expertise across the entire customization lifecycle. When you describe your use case, the AI coding agent activates the relevant skills, guiding it through data preparation and validation, technique selection, hyperparameter configuration, model evaluation, and deployment. Skills provide specialized knowledge about SageMaker AI APIs, ML workflows, best practices, and common patterns, enabling your coding agent to provide more accurate, SageMaker AI-specific guidance, generating ready-to-run notebooks at each step. Skills are fully customizable, so you can modify them to match your team’s workflows, governance standards, and tooling preferences, enabling reproducible organizational best practices, a common challenge with general-purpose coding assistants.

JupyterLab in SageMaker AI includes an integrated agentic development environment support through ACP. By default, Kiro, AI software development agent, is pre-configured in the chat panel, providing AI-powered code completion, debugging assistance, and interactive coding support directly within your JupyterLab environment. When you use coding agents in SageMaker AI JupyterLab, the space automatically loads relevant SageMaker AI model customization Skills into your agent’s context.

Additionally, you can configure other Agent Communication Protocol (ACP) compatible coding agents of your choice, such as Claude Code, giving you flexibility to work with the tools that best fit your workflow. ACP-compatible agents can benefit from the same SageMaker AI Skills integration when used within SageMaker AI JupyterLab. While this example shows the integration with JupyterLab, you can also use remote access to your own IDE outside of JupyterLab.

The SageMaker AI agent skills are built conforming with the Agent Skills open format. The agent-guided model customization workflows are powered by nine modular skills that cover the full customization lifecycle: