Favorite The models powering today’s agents are remarkably capable. They can reason across complex problems, plan multi-step workflows, and generate nuanced responses. But most agents are operating well below that potential. The gap isn’t intelligence. It’s access to the right context and feedback. A customer service agent tasked with answering
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Shared by AWS Machine Learning June 17, 2026
Favorite Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in institutional knowledge that has never been written down. You want to trust the decisions made by your AI agents, but that
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Shared by AWS Machine Learning June 17, 2026
Favorite What if you came back from a full day of meetings and the busywork was already done? Stalled deals followed up on. Compliance changes summarized. Meeting prep written. Not because you multi-tasked, but because something was working in the background while you focused on other urgent priorities. Teams are already using Amazon Quick — an AI assistant
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Shared by AWS Machine Learning June 17, 2026
Favorite Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation. For payloads up to 128,000
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Shared by AWS Machine Learning June 17, 2026
Favorite Research in “Nature” shows our conversational AI system matches primary care physicians in complex disease management. View Original Source (blog.google/technology/ai/) Here.
Favorite As large language models (LLMs) grow in size and complexity, maximizing inference throughput while minimizing latency remains a critical challenge for enterprise production deployments. Speculative decoding is one effective strategy to address this, utilizing a lightweight draft model to guess future tokens which are then verified by the target LLM in a single forward pass.
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Shared by AWS Machine Learning June 16, 2026
Favorite Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up end-to-end latency by up to 2x for generative AI models during scale-out events. Over the years, Amazon SageMaker AI has continued to reduce
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Shared by AWS Machine Learning June 16, 2026
Favorite Today, we’re announcing a new API with Amazon Bedrock Guardrails. With this API, you can apply individual safeguards, also referred to as safety checks, at any point in your agentic AI applications without creating guardrail resources. The new InvokeGuardrailChecks API gives you the flexibility to invoke supported safeguards at
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Shared by AWS Machine Learning June 16, 2026
Favorite A common challenge in AI-powered research workflows is depth versus context. If your agent reads ten web pages, its context window (the amount of text a large language model (LLM) can process at once) gets filled with raw content. If it also runs data analysis code, chart-generation logic competes
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Shared by AWS Machine Learning June 15, 2026
Favorite When your AI agent fails in production, knowing that it failed is only the beginning. The harder question is why it failed and what to fix. Traditional evaluation tells you “this agent scored 60 percent on goal completion,” but leaves you manually reviewing execution traces to understand what went
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Shared by AWS Machine Learning June 15, 2026
Shared by AWS Machine Learning June 17, 2026
