AI

Automated Reasoning checks rewriting chatbot reference implementation

Today, we are publishing a new open source sample chatbot that shows how to use feedback from Automated Reasoning checks to iterate on the generated content, ask clarifying questions, and prove the correctness of an answer. The chatbot implementation also produces an audit log that includes mathematically verifiable explanations for the answer validity and a

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Scale LLM fine-tuning with Hugging Face and Amazon SageMaker AI

Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although foundation models (FMs) offer impressive general capabilities, they often fall short when applied to the complexities of enterprise environments—where accuracy, security, compliance, and domain-specific knowledge are non-negotiable. To meet

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New Relic transforms productivity with generative AI on AWS

New Relic Inc. is a San Francisco-based technology company that pioneered application performance monitoring (APM) and provides comprehensive observability solutions. Serving leading customers worldwide, including major brands like Ryanair, New Relic helps organizations monitor and optimize their digital systems to deliver better customer experiences. New Relic faced a challenge common to many rapidly growing enterprises.

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Accelerate agentic application development with a full-stack starter template for Amazon Bedrock AgentCore

Generative AI and agentic applications are reshaping how businesses operate—from customer support bots to research assistants—and teams need to move from prototype to production quickly. Last year, AWS released Amazon Bedrock AgentCore—a development platform for building, deploying, and scaling AI agents in production. AgentCore provides core building blocks like runtime hosting, memory, tool integration, and

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Agent-to-agent collaboration: Using Amazon Nova 2 Lite and Amazon Nova Act for multi-agent systems

My first attempt at building a travel planning agent looked exactly like most early prototypes: one big model, a few tools, and a long system prompt. It worked fine until the moment real-world complexity showed up. Flights came from a clean API, hotels lived behind a shifting web UI, and the model kept mixing instructions,

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