An AWS blog post walks through building an end-to-end workflow that reads multi-tab RFI Excel workbooks from Amazon S3, extracts and structures questionnaire data via natural-language instructions, and writes CSV output back to S3. It demonstrates how Amazon Quick Automate's agent teams and conversational refinement can automate a manual enterprise process without custom code.
An AWS post outlines best practices for Amazon Quick Automate, a multi-agent automation capability within Amazon Quick. It helps enterprises deploy agents that reason over context and coordinate across departments, systems, UIs, APIs, and third-party applications instead of following rigid scripts.
An AWS post outlines best practices for Amazon Quick Automate, a multi-agent automation capability within Amazon Quick. It helps enterprises deploy agents that reason over context and coordinate across departments, systems, UIs, APIs, and third-party applications instead of following rigid scripts.
Amazon Quick Automate announced native case management capabilities to handle enterprise-scale agentic workflows. The feature tracks state across millions of work items, surfaces failure points, enables human handoff, and dynamically scales infrastructure.
Amazon Quick Automate announced native case management capabilities to handle enterprise-scale agentic workflows. The feature tracks state across millions of work items, surfaces failure points, enables human handoff, and dynamically scales infrastructure.
AWS launched model caching for Amazon SageMaker Inference on HyperPod, pre-loading model weights and container images onto cluster nodes' local NVMe storage before pods request them. Pods can then serve traffic in seconds instead of the 25-30+ minutes cold downloads take for large models like DeepSeek-R1. The feature is generally available in all HyperPod regions and enabled via a modelCacheConfig section in existing deployment specs.
AWS launched model caching for Amazon SageMaker Inference on HyperPod, pre-loading model weights and container images onto cluster nodes' local NVMe storage before pods request them. Pods can then serve traffic in seconds instead of the 25-30+ minutes cold downloads take for large models like DeepSeek-R1. The feature is generally available in all HyperPod regions and enabled via a modelCacheConfig section in existing deployment specs.
Amazon Bedrock AgentCore Browser Tool, combined with Strands Agents, provides a fully managed browser service that lets AI agents automate legacy web applications through secure, isolated sessions. It targets enterprises in healthcare, manufacturing, retail, and financial services where standard RPA falls short.
Amazon Bedrock AgentCore Browser Tool, combined with Strands Agents, provides a fully managed browser service that lets AI agents automate legacy web applications through secure, isolated sessions. It targets enterprises in healthcare, manufacturing, retail, and financial services where standard RPA falls short.
KTern.AI built agentic AI agents on Amazon Bedrock AgentCore to automate SAP digital transformation workflows. The agents autonomously handle reverse engineering, fit-to-standard analysis, code analysis, and exception mining in Finance and Sales processes, reducing the need for custom agent infrastructure.
KTern.AI built agentic AI agents on Amazon Bedrock AgentCore to automate SAP digital transformation workflows. The agents autonomously handle reverse engineering, fit-to-standard analysis, code analysis, and exception mining in Finance and Sales processes, reducing the need for custom agent infrastructure.