AI/ML news, top picks, and generated innovation digests.
97 articles tagged with this keyword, sorted by most recent first.
Set up Amazon Bedrock AgentCore Observability for AI agents running outside AWS: on-premises, on GCP, on Azure, or on developer machines. This walkthrough uses the AWS Distro for OpenTelemetry (ADOT) and IAM credentials to route session traces, span metrics, and token usage to the same AgentCore Observability dashboard.
Learn how to automate legacy web applications that need human-like interaction using Amazon Bedrock AgentCore Browser Tool and Strands Agents. This walkthrough covers a reference architecture for an AI-powered digital worker that drives legacy interfaces through secure, isolated browser sessions while preserving human oversight and full audit trails.
Learn how to build a multi-agent M&A due diligence system on Amazon Bedrock AgentCore. This post walks through a reference architecture that combines agent orchestration, knowledge retrieval, and governance controls, then deploys a complete sample you can run in your own AWS account.
Learn how to visualize and analyze Amazon Bedrock cost attribution using Amazon Athena and CUDOS dashboards. This post shows how to set up CUR 2.0 with IAM principal data, query Bedrock spend by principal, project, and team, and build dashboards to track AI costs across your organization.
Twitch users can now opt out of allowing their content to be used to train Amazon's generative AI models. Opting out means that "your streams, VODs, clips, stream chats, and pictures and text on your channel" won't be used in "future training" of an Amazon AI model "whose purpose is to generate or synthesize text, […]
Solv Labs built a governed agent-payments workflow on Amazon Bedrock AgentCore payments, where every transaction is authorized, attested in an AWS Nitro Enclave, priced for risk, and anchored to a public blockchain before settlement. See how the pattern gives enterprises a verifiable, auditable trail for autonomous agent payments in regulated environments.
Daybreak Red and Daybreak Blue from OpenAI, specialized cyber defense models from OpenAI, are now available on Amazon Bedrock to eligible customers. Both models run with zero-operator access enforced at the chip, keeping your code and vulnerability data secure.
Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in.
Claude apps gateway is a self-hosted governance layer between Claude Code and Claude Desktop and Amazon Bedrock or Claude Platform on AWS. This post presents a production reference deployment covering end-to-end architecture, enterprise deployment patterns, cost, and implementation resources.
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.
TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.
Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.
Learn how to configure rate limits on Amazon Bedrock AgentCore gateway to enforce per-user and per-target traffic controls. Define request, token, and connection limits scoped by JWT claims or IAM identity to protect downstream models, tools, and agents from traffic spikes.
Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior.
As engineering teams adopt coding agents like Codex, leaders need visibility into adoption, consumption, and reliability. This post shows how to route Codex OpenTelemetry metrics through a local collector to Amazon CloudWatch for an AWS native view of usage by user, team, and cost center.
A regulated customer needed all Claude Code inference processed in a single AWS Region (London), not just in-geography. This post shows two ways to pin Claude Code on Amazon Bedrock to one Region: an application inference profile or the Mantle endpoint, paired with an IAM Region condition, plus how to verify compliance in AWS CloudTrail.
Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow.
PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock.
Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.
In this post, we'll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.
AI agents on Amazon Bedrock AgentCore run in the cloud, but users' tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required.
Amazon Bedrock AgentCore harness is now generally available. Learn how to add it as an agent step in n8n workflows using a new open-source community node, and build agents with persistent memory, real tools, code execution, and VPC isolation — all from the n8n editor with no infrastructure or agent code.
Today, we are introducing the general availability of Web Search on Amazon Bedrock. It is a server-side built-in tool that grounds model responses in current web knowledge. With Web Search, grounding becomes a native capability of Amazon Bedrock, with no third-party vendors to onboard, no external APIs to orchestrate, and no additional third party vendor security reviews to conduct. In this post, we walk through what Web Search on Amazon Bedrock is, why it matters, how to enable it using the OpenAI Responses API, and how to get started with the tool.
Extracting insights from dozens of websites by hand quickly becomes overwhelming. This post shows how to build an automated web insight extraction solution with Amazon Bedrock AgentCore Browser, Amazon Bedrock, Amazon OpenSearch Serverless, and AWS Lambda that monitors RSS feeds, renders pages reliably, and makes AI-extracted insights searchable.
Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate.
Amazon Bedrock now supports automatic Automated Reasoning policy refinement. The refinement engine diagnoses failing tests and proposes formal-logic fixes for rule issues and language issues, and you approve every change before it takes effect. This post walks through both refinement modes with complete API and console workflows.
As your AI agents move from prototype to production, the challenge shifts from getting them to work to keeping them fast and efficient. Learn how to use Amazon Bedrock AgentCore Observability and Amazon CloudWatch to find performance bottlenecks and diagnose memory issues in long-running agent sessions.
In this post, we demonstrate how Yahoo implemented Amazon Bedrock to enhance their Search Retargeting (SRT) capabilities in the Yahoo DSP ad tech suite. SRT is a core audience targeting solution that helps advertisers reach users based on their historical search behavior, bridging search intent with display, video, and native advertising. Beyond targeting keywords entered on Yahoo Search, SRT uses AI to identify and engage users who demonstrate intent through search activity both on Yahoo and across integrated partner systems.
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost.
Amazon Bedrock Advanced Prompt Optimization optimizes your prompts for up to 5 models at once and compares original versus optimized performance across quality, latency, and cost. Migrate to a new model or improve your current one in minutes instead of weeks.
This post explains how Private Key JWT client authentication works in AgentCore Identity and reviews the supported grant flows. We then walk through creating an AWS KMS signing key, registering its public key with your identity provider, configuring a credential provider on the AWS Management Console, and reviewing example AWS CloudTrail events that record your agent’s access.
Learn how Amazon Bedrock AgentCore delivers autonomous, cross-system business intelligence through configuration rather than custom code. Using pre-built MCP server connectors, fine-grained access control, and persistent memory, enterprises can query multiple data sources with natural language while enforcing role-based boundaries automatically.
The Model Context Protocol (MCP) published its 2026-07-28 specification, the largest revision since launch: MCP is now stateless, with a governed extensions system and hardened authorization. Learn what changed and how to enable the new version on Amazon Bedrock AgentCore Gateway with a single UpdateGateway call.
Learn how to architect and deploy a production-ready multi-agent AI system using LangGraph for workflow orchestration and Strands for agent reasoning on Amazon Bedrock AgentCore. This post walks through a market surveillance example with state-driven orchestration, checkpoint-based recovery, and AgentCore memory and observability.
In this post, we explore how Guardoc Health uses the Amazon Nova family of models, available through Amazon Bedrock, to transform clinical documentation in long-term care.
This post covers Opus 5’s improvements and practical guidance for AI engineers integrating the model into agentic systems and production inference workloads on Amazon Bedrock. See the documentation for Claude Platform on AWS.
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. Learn how to select a model, run inference through the Responses API on the bedrock-mantle endpoint, reduce cost with prompt caching, connect the OpenAI Codex coding agent, and plan for quotas and scaling.
In this post, we explain how Amazon Bedrock Guardrails can be configured for code generation workflows with coding assistants to overcome these constraints. With these best practices, you can build an efficient blueprint helping you with effective capacity planning with robust safety coverage.
Together, Motorway and AWS built an end-to-end evaluation pipeline that reduced incorrect results from 1 in 8 queries to 1 in 50 and cut issue detection time from few hours to few minutes. The pipeline combines the Strands Agents SDK with Amazon Bedrock AgentCore, a fully managed service for deploying and operating AI agents at scale. In this post, you will learn how to build this pipeline for your own agents.
In this post, we explore how Jefferies overcame these challenges with a solution built on Strands Agents, an agent harness SDK for building AI agents that can reason, plan, and act by orchestrating calls to foundation models (FMs) and external tools. The solution uses large language models (LLMs), Amazon Bedrock, and Amazon Bedrock Knowledge Bases. It also uses Model Context Protocol (MCP), an open standard that helps AI agents securely connect to diverse data sources and tools through a unified interface. We cover the solution overview, the rationale for selecting the underlying technology stack, lessons learned, and the business impact the solution created at Jefferies.
Amazon Bedrock AgentCore optimization surfaces silent behavioral failures in production AI agents: the ones that pass every health check but still deliver wrong outcomes. Learn how insights discovers, explains, and ranks failure patterns across sessions so you can fix the highest-impact issues first.
This post focuses on why classic retrieval falls short on multi-part questions, how the AgenticRetrieveStream API works (including request construction and trace parsing), and when to choose it over the standard Retrieve API.
AI Teammates are agentic AI on Amazon Bedrock, and few engineering organizations run them in production at the scale that monday.com does. Nine in ten Builders use AI coding tools every month, up from roughly half a year ago. Per-engineer PR throughput is up by more than half. Every figure in this post comes from monday’s own internal production data. In this post, we share the architecture behind those numbers, the retrofits that made it work in a decade-old code base, and the confidence-scored merge play closing the gap to full autonomy.
AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models. The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post . That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said. “Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra , chief revenue officer at IT consulting firm Kanerika. The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added. That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain , principal analyst at Pareekh Consulting. Richer billing data to boost enterprise AI cost gover…
AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models. The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post . That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said. “Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra , chief revenue officer at IT consulting firm Kanerika. The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added. That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain , principal analyst at Pareekh Consulting. Richer billing data to boost enterprise AI cost gover…
This post describes how Couchbase adopted Amazon Bedrock to power Capella iQ with Anthropic’s Claude family of models, the architectural decisions behind their multi-model approach, and the operational benefits realized in production.
In this post, we walk through the three pillars that make this possible: simplified setup, smarter retrieval, and production readiness. We also show you code examples for setting up a knowledge base and retrieving from it.
This post covers what makes Grok 4.3 a great fit for agentic and enterprise workloads, how you access it through Amazon Bedrock, and how to use the capabilities most teams reach for first: a basic chat request, configurable reasoning effort, tool calling, structured output, image input, and stateful multi-turn conversations.
In this post, we show you how to build a voice ordering system that answers a phone number and takes the order from greeting to confirmation. The system uses Amazon Bedrock AgentCore to host and run the agent and Amazon Nova 2 Sonic for real-time speech, connected to a restaurant backend through the Model Context Protocol (MCP). The walkthrough covers deploying the full stack with AWS Cloud Development Kit (AWS CDK) and bridging a phone call into the agent through a Session Initiation Protocol (SIP) gateway on Amazon Elastic Container Service (Amazon ECS) and AWS Fargate. It also warms the agent session while the phone is still ringing, so the caller never hears dead air.
In this post, we walk you through the Computer Vision MCP Server, which illustrates this approach, representing how AI systems can process visual information and make intelligent decisions through a single, standardized interface. This convergence transforms what was once a complex integration challenge into a streamlined process, making AI capabilities accessible to a broader range of applications and developers.
This post shows how Thrad.ai deployed a multi-agent system with Strands Agents and Amazon Bedrock AgentCore that automates the pipeline from prospect discovery through personalized email generation. The post compares two orchestration patterns (Swarm and Graph) with head-to-head benchmarks on latency, cost, and email quality. You’ll also learn how the system scores prospects using weighted criteria, intent classification, and temporal decay, plus governance controls for production deployment.
In this post, we share how Flo Health’s engineering team turned a proof of concept (PoC) from the AWS Generative AI Innovation Center into a production-grade, AI-powered medical content review and generation system built on Amazon Bedrock. T
In this post, you will learn how ScienceSoft, an Amazon Web Services (AWS) Services Partner, integrated Amazon Nova 2 Sonic with Amazon Bedrock Guardrails to build a Health Insurance Portability and Accountability Act (HIPAA)-compliant AI voice scheduler. You will see how the solution addresses healthcare scheduling challenges while maintaining privacy, compliance, and responsible AI standards, and how you can apply the same architecture to your own workflows.
Today, GPT-5.6 Sol, Terra, and Luna from OpenAI are generally available on Amazon Bedrock, bringing the smartest family of models from OpenAI yet to Amazon Bedrock’s next-generation inference engine built for high-performance, security and reliability.
In this post, we describe how Bluesight used two AWS engagements and Amazon Bedrock AgentCore to evolve from a single-product AI prototype to Prism, a unified agentic AI solution spanning six healthcare compliance products. Prism Assistant for ControlCheck launched in May 2026 and is already in use by 20 health systems. A more complex multi-product agentic solution is on track for later in 2026.
Building multi-tenant agents with Amazon Bedrock AgentCore and Apply fine-grained access control with Bedrock AgentCore Gateway interceptors establish the conceptual foundation for on-behalf-of (OBO) token exchange in agentic systems. This post is the implementation guide. It walks through a complete multi-tenant OBO setup against Okta, shows the JSON Web Token (JWT) claim transformations on each hop, and demonstrates how audience binding produces defense in depth that scales across tenants.
In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without extract, transform, and load (ETL). The same Stardog deployment works behind AWS computes (Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS Lambda). We use AgentCore here because it bundles inbound auth, hosting, and tool credentials into one managed service.
Evolving from a traditional software as a service (SaaS) platform into a next-generation agentic AI platform meant orchestrating multiple specialized agents across long-running enterprise programs. Each agent operates with persistent context, secure tool access, and production-grade reliability. We built that system on Amazon Bedrock AgentCore using the Strands Agents SDK. This post walks through how we architected it, which agents we built, and the outcomes for our customers.
Today, we're announcing the Claude apps gateway for AWS, a self-hosted control plane that gives organizations a single point of control over access, cost, and policy for Claude Code and Claude Desktop. In this post, we show how to set up and run Claude apps gateway for AWS with Amazon Bedrock and Claude Platform on AWS.
In this post, we show how organizations in the public sector can automate their email management using a generative AI solution powered by Amazon Bedrock.
In this post, you build and connect that server end to end. You will implement MCP tools, set up two-layer JSON Web Token (JWT) authentication, deploy with AWS Cloud Development Kit (AWS CDK), and connect the result to Mistral AI’s Vibe. The post also covers prerequisites, solution architecture, best practices for MCP servers and Vibe connectors, and resource cleanup. The ecommerce server that you build supports product search, order placement, review submission, and returns processing using Amazon DynamoDB for data and Amazon Cognito for identity management.
This post shows you two architecture patterns that address this problem. Both use an internet-facing ALB with AWS WAF and route traffic through a VPC Interface Endpoint to AgentCore Runtime. Pattern 1 places an AWS Lambda proxy between the ALB and the VPC Endpoint, giving you full control over request transformation. Pattern 2 targets the VPC Endpoint ENI IP addresses directly from the ALB, removing the Lambda hop entirely. You also learn how to close the direct-access backdoor with a resource policy so that traffic flows through AWS WAF only. Both patterns have been tested end-to-end with SigV4 and OAuth (Amazon Cognito JWT) authentication.
In this post, we show how you can use Jamf’s AI Governance with Amazon Bedrock to configure, deploy, and validate managed settings for AI applications across a Mac fleet.
This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image editing tools, and a React frontend, with a single deployment command. The infrastructure is defined using AWS Cloud Development Kit (AWS CDK).
In this post, you build an AWS Support Companion using Amazon Bedrock AgentCore. The agent uses Strands Agents as the orchestration framework and connects to AWS services through the Model Context Protocol (MCP). By the end, you have a working agent that can analyze CloudWatch logs, search AWS documentation, query community knowledge from AWS re:Post, and create support cases, all from a single conversational interface. The solution deploys with a single script using AWS CloudFormation and includes a web frontend built on AWS Amplify for interacting with the agent.
In this post, we walk through how to get started with MiniMax models on Amazon Bedrock, including the capabilities supported by these models, the service tiers available, how on-demand inference scales to handle your workloads, and the different APIs you can use to access them. Using these models, customers can build agentic applications, long-context document analysis pipelines, and software engineering workflows, all backed by the security and operational guarantees of AWS.
AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.
Social engineering through phishing remains one of the most common tactics for launching cyberattacks. AI-generated phishing email messages now pose a new challenge for security teams managing email systems, significantly raising the risk because of their advanced sophistication. Modern social engineers use generative AI and open source intelligence (OSINT) to craft thousands of unique messages […]
AWS has increased key Amazon Bedrock AgentCore runtime quotas by up to fivefold, enabling enterprises to support more concurrent AI agents and user interactions without going through the quota-increase process that often slows production deployments. While quota increase service requests are free themselves, the added capacity is more likely to translate into higher underlying compute and runtime consumption as enterprises expand AI deployments. “The new default limits support up to 5,000 active concurrent sessions in US East (N. Virginia) and US West (Oregon), and 2,500 in all other supported Regions (previously 1,000 and 500 respectively),” AWS wrote in its release notes . The hyperscaler has also increased the number of interactions each AI agent can handle from 25 tokens per second to 200 tokens per second across all supported regions , which it says will enable enterprises to support more simultaneous user requests. Further, to help enterprises scale AI applications faster during periods of peak demand, the hyperscaler also quadrupled the rate at which new AI agent sessions can be created for container deployments, increasing the limit from 100 TPM to 400 TPM. Why the higher quotas matter for enterprise AI deployments The change in AgentCore Runtime quotas, according to Charlie Dai , principal analyst at Forrester, is the hyperscaler’s response to enterprises rapidly shifting AI-agent experiments to production deployments: “In our client conversations, the bigger change…
AWS is offering to help enterprises address the growing cost of retaining telemetry for talkative AI applications with a new engine for its managed Amazon OpenSearch Service optimized for log analytics, which it claims can reduce storage costs by 70% and at the same time deliver better price-performance. AI and agentic applications are generating more telemetry than conventional observability architectures were built to manage economically, forcing enterprises to balance retaining the operational data needed for security, compliance and incident response against rising related infrastructure costs. The new engine will allow customers to continue using the same management console, APIs, security model and networking configuration as the service’s existing general-purpose engine, while storing data in Apache Parquet format and maintaining Lucene search indexes for searchable fields, AWS said. It uses Apache Calcite to parse and optimize queries before routing analytical operations to Apache DataFusion and search predicates to Lucene, allowing search and analytical aggregation to run within the same query, AWS executives wrote in a blog post. The optimized engine supports SQL and Piped Processing Language (PPL), they said. Keeping costs down without losing detail In a recent survey of enterprises’ log management practices, Dynatrace found that AI workloads drove a 93% increase in log volume over the previous year, organizations to exclude an average of 86% of log data to manage…
AWS is offering to help enterprises address the growing cost of retaining telemetry for talkative AI applications with a new engine for its managed Amazon OpenSearch Service optimized for log analytics, which it claims can reduce storage costs by 70% and at the same time deliver better price-performance. AI and agentic applications are generating more telemetry than conventional observability architectures were built to manage economically, forcing enterprises to balance retaining the operational data needed for security, compliance and incident response against rising related infrastructure costs. The new engine will allow customers to continue using the same management console, APIs, security model and networking configuration as the service’s existing general-purpose engine, while storing data in Apache Parquet format and maintaining Lucene search indexes for searchable fields, AWS said. It uses Apache Calcite to parse and optimize queries before routing analytical operations to Apache DataFusion and search predicates to Lucene, allowing search and analytical aggregation to run within the same query, AWS executives wrote in a blog post. The optimized engine supports SQL and Piped Processing Language (PPL), they said. Keeping costs down without losing detail In a recent survey of enterprises’ log management practices, Dynatrace found that AI workloads drove a 93% increase in log volume over the previous year, organizations to exclude an average of 86% of log data to manage…
We're excited to introduce US-based frontier open-weight models in AWS GovCloud (US). With this release, Amazon Bedrock now supports OpenAI’s open-weight GPT OSS models (120B and 20B) and NVIDIA Nemotron (Nano 9B v2, Nano 12B v2, Nano 30B, Super 120B) models. In this post, we cover these models and their capabilities, the inference options for data residency, the available service tiers and how to get started.
In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality, Amazon Neptune Analytics for advanced graph algorithms including Personalized PageRank, and Amazon Titan Embeddings for vector representations. This implementation showcases how to build and deploy HippoRAG within AWS infrastructure for enterprise-scale applications.
In this post, you will learn how Inscribe developed an agentic AI system using Amazon Bedrock that reasons across documents the way an expert fraud analyst would. With this new agentic AI system, Inscribe now detects tampered, fabricated, and AI-generated financial documents in under 90 seconds. This is a 20x improvement over traditional manual review, while maintaining the accuracy and explainability required by financial services regulations.
The Amazon Bedrock Model Profiler is an open source tool that aggregates model metadata from multiple AWS APIs and external sources into a single, searchable interface. In this post, you’ll learn what the Model Profiler provides, the real-world scenarios it supports, and how to deploy it in your own environment in under five minutes.
It’s our goal for AWS to be the most secure place to run any workload, and in support of that we’ve been deeply investing in security across our services since AWS's inception more than two decades ago. Our AI services like Amazon Bedrock are built on this foundation and with the same focus.
Today, we’re excited to announce the availability of Anthropic’s most advanced Sonnet model, Claude Sonnet 5, on Amazon Bedrock and Claude Platform on AWS. Claude Sonnet 5 is the first Sonnet model of Anthropic’s latest generation and represents a meaningful step forward. It delivers top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional […]
This post walks through how AG-UI integrates into the Fullstack AgentCore Solution Template (FAST) to build interactive agent frontends on Amazon Bedrock AgentCore. We then show how CopilotKit extends this with generative UI, shared state, and human-in-the-loop interactions, all deployed on Amazon Bedrock AgentCore.
In this post, we show you how to use managed entitlements for Amazon Bedrock to subscribe once from a central account and distribute model access across your organization. This approach removes the need for AWS Marketplace permissions in workload accounts.
In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM gateway. These patterns address real-world challenges such as quota exhaustion during unexpected traffic surges, maximizing availability through geographic distribution of inference, and helping prevent noisy neighbor problems in multi-tenant environments.
In this post, we share the technical approach using token-based distillation, lessons learned, and deployment architecture. If you face similar bilingual NER challenges, you can benefit from IBS Software’s experience with the Amazon Bedrock knowledge distillation capabilities.
In this post, we show how pairing Amazon Nova 2 Lite with Anthropic’s Claude Sonnet 4.6 delivers an efficient solution for digitizing scanned documents at scale. We built a two-model pipeline on Amazon Bedrock for digitizing scanned yearbook pages. Amazon Nova 2 Lite handles native multimodal extraction in a single call: detecting photos, extracting visible names with coordinates, and returning page-level metadata. Claude Sonnet 4.6 then performs spatial reasoning to match names to faces based on page layout.
In this post, we show you how PAR built a production-ready multi-tenant LLM analytics system that enforces row-level security through a three-layer architecture: cryptographic request signing with AWS SigV4, semantic validation on Amazon Bedrock, and programmatic data isolation via Split-Plane SQL. We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.
In this post, we show you how to build an automated claims processing pipeline using two key Amazon Bedrock capabilities: Amazon Bedrock Data Automation for intelligent document extraction from healthcare claim forms, and Amazon Bedrock AgentCore for hosting an AI agent that validates and transforms the extracted data into FHIR (Fast Healthcare Interoperable Resources) resources in AWS HealthLake. You will learn how to combine these services to create an end-to-end workflow that reduces manual processing while maintaining accuracy through automated validation checks.
In this post, you learn how to debug production agent failures using built-in observability capabilities. We walk through common failure patterns, show how to analyze agent behavior with traces and metrics, and provide structured workflows for resolving issues such as infinite loops and tool invocation failures. This is Part 1 of a two-part series. Part 2 covers performance optimization and memory management.
This article is brought to you by Capital One . After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives. For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy. That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructu…
In this post, we show you how to build Chaplin (Customer Health and Planned Lifecycle Intelligence Nexus), an open source solution that uses AI agents exposed through the Model Context Protocol (MCP) to provide self-service health event analytics.
In this post, you will learn how to build a voice agent that handles appointment reminder conversations using Amazon Nova 2 Sonic and Amazon Bedrock AgentCore. The agent authenticates patients by voice, manages appointments (confirm, cancel, or reschedule), collects pre-visit health information, and escalates to human staff when needed. You handle routine calls at scale, which can help reduce no-show rates. This sample focuses on the agentic side of the problem: voice conversation and tool orchestration. A browser-based interface is included for testing. To connect the agent to actual phone lines for outbound dialing, you would integrate a telephony service such as Amazon Connect Customer.
This post shows you how to build a conversational protein research assistant that combines three capabilities: Natural language query parsing to extract structured search parameters, vector similarity search over protein embeddings using a specialized language model and ai-generated scientific summaries of search results.
In this post, you will learn patterns for implementing production-ready multi-tenant systems using Amazon Bedrock AgentCore. You will see these patterns demonstrated through healthcare AI agents that serve multiple clinics and hospitals.
In this post, you will learn how Ampersend built a pay-per-intelligence routing layer on top of Amazon Bedrock AgentCore Payments. AI agents autonomously route tasks to the most effective model, pay per request, and operate within spending budgets. You will also see how the two-hop payment pattern works end-to-end and how to get started with your own implementation.
In this post, we walk through the problem space, our architecture on Amazon Bedrock and Amazon OpenSearch Serverless, the evaluation methodology we built on OpenStreetMap ground truth, four experiments that compared embedding models, fusion strategies, captioning, and search methods, and the practical guidance you can apply when building a similar system. You’ll learn which design choices move the needle for geospatial semantic search, including why Amazon Nova Multimodal Embeddings delivered the highest F1 scores across both benchmark queries in our evaluation. The work described here evolved into Vexcel Intelligence, a searchable imagery product.
Web Search on Amazon Bedrock AgentCore is now generally available. In this post, we walk through what makes Web Search on Amazon Bedrock AgentCore different, why it matters, and how to wire it in with a few lines of code.