Lauren Mullennex

Agentic tool calling is what makes AI agents useful in production. It’s how they query databases, trigger workflows, retrieve real-time data, and act on a user’s behalf. But base models frequently hallucinate tools, pass bad parameters, and attempt actions when they should ask for clarification. These failures erode trust and block production deployment. You can use Serverless model customization in Amazon SageMaker AI to fix these problems without managing infrastructure.

AWS Big Data Blog Developers and machine learning (ML) engineers can now connect directly to Amazon SageMaker Unified Studio from their local Visual Studio Code (VS Code) editor. With this capability, you can maintain your existing development workflows and personalized integrated development environment (IDE) configurations while accessing Amazon Web Services (AWS) analytics and artificial intelligence and machine learning (AI/ML) services in a unified data and AI development environment.

Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models.