AWS Bedrock is a fully managed service that allows developers to build and scale generative AI applications without needing to manage infrastructure or train foundational models themselves.
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tech22025-12-24
The article discusses the challenges enterprises face with manual workflows across multiple web applications and introduces AI agent-driven browser automation as a solution. It highlights how AI agents can intelligently navigate complex workflows, reduce manual intervention, and improve operational efficiency. The article provides a detailed example of an e-commerce order management system that utilizes Amazon Bedrock and AI agents for automating order processing across various retailer websites. It emphasizes the importance of human oversight in handling exceptions and maintaining compliance.
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tutorial2026-01-15
This article presents a solution for automating business reporting using generative AI and Amazon Bedrock. It highlights the inefficiencies of traditional reporting processes and introduces a serverless architecture that leverages AWS services to streamline report writing and enhance internal communication. The solution includes a user-friendly interface for associates and managers, enabling efficient report generation and submission. Additionally, it addresses challenges such as data management and risk mitigation associated with AI implementation.
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project2025-12-23
The article discusses the collaboration between AWS and Visa to introduce Visa Intelligent Commerce, which leverages Amazon Bedrock AgentCore to enable agentic commerce. This new approach allows for seamless, autonomous payment experiences that reduce manual intervention in transactions. The article explains how intelligent agents can handle multi-step tasks in various sectors, particularly in payments and shopping, transforming traditional workflows into more efficient, outcome-driven processes. It also highlights the technical architecture and tools involved in building these agentic workflows.
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tech12026-01-14
The article discusses how Slack developed a comprehensive metrics framework to enhance the performance and cost-efficiency of their Apache Spark jobs on Amazon EMR. By integrating generative AI and custom monitoring tools, they achieved significant improvements in job completion times and cost reductions. The framework captures over 40 metrics, providing granular insights into application behavior and resource usage. The article outlines the architecture of their monitoring solution and the benefits of AI-assisted tuning for Spark operations.
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tech22025-12-22
The article discusses the Chain-of-Draft (CoD) prompting technique, which offers a more efficient alternative to the traditional Chain-of-Thought (CoT) method for large language models. CoD reduces verbosity and improves cost efficiency and response times by limiting reasoning steps to five words or less. The authors demonstrate the implementation of CoD using Amazon Bedrock and AWS Lambda, showcasing significant reductions in token usage and latency while maintaining accuracy. The article emphasizes the practical benefits of CoD for organizations scaling their generative AI implementations.
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project2025-11-20
The article discusses how Care Access, a healthcare organization, utilized Amazon Bedrock's prompt caching feature to significantly reduce data processing costs by 86% and improve processing speed by 66%. By caching static medical record content while varying analysis questions, Care Access optimized their operations to handle large volumes of medical records efficiently while maintaining compliance with healthcare regulations. The implementation details, including the architecture and security measures, are also highlighted, showcasing the transformative impact of this technology on their health screening program.
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product2025-11-18
The article discusses how organizations can leverage platform engineering principles to accelerate the development and deployment of generative AI applications. It highlights the challenges faced by organizations in experimenting with generative AI and emphasizes the importance of building reusable components to manage costs and improve efficiency. The article outlines the architecture of generative AI applications, including the integration of various data layers and the role of large language models. It also covers best practices for observability, orchestration, and governance in AI workflows.
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vision2025-11-17
The article introduces AWS Professional Services' new approach to consulting, leveraging agentic AI to enhance cloud adoption and digital transformation for organizations. It highlights the role of specialized AI agents in streamlining consulting processes, improving solution quality, and reducing project timelines. The integration of AI with human expertise is emphasized as a means to deliver better customer outcomes. Real-world examples, including the NFL's use of AWS agents, illustrate the tangible benefits of this innovative consulting model.
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product2025-10-30
The article discusses the challenges AI agents face when browsing the web, particularly with CAPTCHAs and other bot detection mechanisms. It introduces Amazon Bedrock AgentCore Browser's new feature, Web Bot Auth, which provides AI agents with verifiable cryptographic identities to reduce CAPTCHA friction. The article explains how this protocol works and its collaboration with WAF providers to ensure secure access for verified bots. It highlights the benefits for both AI agents and website owners in managing automated traffic.
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product2025-05-20
Interested to know when an Amazon Bedrock knowledge base for the Redshift database, how to open the access for Apps with nature language.
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project2025-02-18
Very classic Glue job pipeline to feed the AWS Bedrock Knowledge Bases for a RAG use case.
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vision2025-02-12
summarise of all the concept and technologies to build a production ready RAG solution.
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project2024-12-17
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product2024-11-15
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product2024-12-13
An agent breaks down the process of answering questions into multiple steps, and uses different tools to answer different types of questions or interact with multiple data sources, is a good practise.
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