Agentic AI is changing how you work, and vector search powers the retrieval layer that makes agents accurate, contextual, and grounded in real data. Agents plan, reason, and take action across multi-step workflows, making fast, relevant access to your organization’s knowledge essential. That knowledge already has a home across databases, object stores, search engines, and … Read more
If you manage Fast Healthcare Interoperability Resources (FHIR) APIs, you must balance open patient data access with strict data protection requirements. Static security rules require constant updates as clinical workflows evolve, and maintaining them manually creates compliance gaps. With Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) through a single … Read more
When an AI agent uses Web Search to ground its answers on behalf of a customer, the organization behind that agent needs domain and date filters to control which sources the agent consults and how fresh those sources must be. A financial-services agent shouldn’t ground its answers in an unvetted blog. A product-information agent shouldn’t … Read more
Mortgage lending runs on documents. Every loan starts with a familiar set: earnings statements, W-2s, bank statements, driver’s licenses, voided checks, and insurance applications. Every lender processes them at scale. The challenge of classifying, extracting, and validating high volumes of documents isn’t unique to mortgage lending. Organizations in banking, insurance, healthcare, and the public sector … Read more
Asynchronous invocation patterns for Amazon Bedrock AgentCore agents in serverless pipelines remove idle compute costs while your AI agent processes requests. A common example is document validation: in a real-estate financing back office, an agent can read a property record or loan contract, reason about whether the information is complete and consistent, and return a … Read more
Fanatics Betting and Gaming (FBG) built a multi-agent customer support system on AWS to solve a challenge unique to sports betting. Customers expect instant, accurate answers, especially during live events when every minute counts. Customers ask about account issues, deposit limits, state-specific regulations, and responsible gaming resources. The rules vary across every jurisdiction where an … Read more
KnowledgeForge is about mining gold from the IT Service Management (ITSM) ticket graveyard: the resolved incident tickets whose knowledge never reaches a knowledge base article. Enterprise IT support teams resolve thousands of tickets every month, and each one holds something useful: a symptom, a root cause, and the fix an engineer applied. That knowledge stays … Read more
Agents have evolved from simple chat applications to autonomous, long-running systems that dynamically discover and compose dozens of tools per task without human oversight. On the other side, service and content providers are moving from human-centric subscription-based, one-size-fits-all pricing to pay-per-use, per-execution models where costs are often a few cents. Today, agents are doing a … Read more
Amazon Quick embedded chat provides a conversational AI interface that you can integrate directly into your web application. Your users can ask questions, explore data, and get insights without leaving your application. However, a generic chat interface creates a disjointed experience. The chat interface must look and feel like a natural part of your application, … Read more
Vector-prompt classification on Amazon Bedrock helps insurance companies accurately classify thousands of daily documents: policies, affidavits, endorsements, and regulatory forms, for compliance, claims, and customer service. Manual classification is time-consuming and error-prone, while traditional automated approaches struggle with documents that look similar but serve different purposes. A policy endorsement and a regulatory affidavit might contain … Read more
If you’re managing a real-time feature store, you might be facing challenges such as data duplication, feature engineering, feature consistency, manual deployment, and latency. Jumio is an identity verification provider that helps businesses detect fraud and build digital trust. To provide these services in real time, Jumio’s machine learning (ML) models needed a real-time feature … Read more
Enterprises rely on large volumes of complex legal agreements to make critical business decisions — determining rights, renewal options, geographic restrictions, and compliance obligations. In industries like entertainment and media, where organizations manage thousands of contracts across multiple jurisdictions, this work remains largely manual: time-consuming, costly, and difficult to scale. Our AI-Driven Annotation (AIDA) solution, … Read more
Independent Software Vendors (ISVs) are expanding their offerings and adding AI agents. Common considerations for organizations when adding agentic workloads include security, scalability, time to market, and cost tracking. ISVs have another dimension, they provide services to other organizations and need to manage agentic workloads for each customer. As such, ISVs need to manage those … Read more
NVIDIA Nemotron 3.5 Lightning is designed for the fast, specialized model execution required by high-volume agentic workloads. With NVIDIA Nemotron 3.5 Lightning on Amazon SageMaker JumpStart, you can access an open model designed for high-volume agentic workloads. With this launch, you can deploy Nemotron 3.5 Lightning from Amazon SageMaker JumpStart without configuring the serving infrastructure … Read more
This post is a collaboration between AWS and the OpenClaw Foundation. Autonomous agents that browse the web, call APIs, and query Model Context Protocol (MCP) servers can encounter services that require an HTTP 402 Payment Required response to be settled before access continues. To use those services without pausing for a human at every transaction, … Read more
In multi-turn reinforcement learning (RL), your custom reward function decides what the model actually learns. A subtly wrong reward can quietly teach the wrong thing while every training curve looks healthy. Designing a reward that holds up over multi-turn, agentic tasks is one of the hardest parts of customizing Amazon Nova models. For multi-turn training, … Read more
A common challenge in building agentic workflows is mixing managed foundation models (FMs) with your own cost-optimized or domain-specific models, without rewriting your agent framework to do it. In this post, we show you how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore, and … Read more
When you deploy AI agents built with frameworks like Strands Agents, LangGraph, and CrewAI, you need observability into their performance. This holds true whether they run on Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), AWS Lambda, on-premises, or another cloud provider such as Google Cloud Platform (GCP) or Microsoft Azure. … Read more
Enterprises across healthcare, manufacturing, retail, and financial services struggle to automate legacy web applications that demand human-like interaction beyond what standard Robotic Process Automation (RPA) can provide at scale. Amazon Bedrock AgentCore Browser Tool, combined with Strands Agents, addresses this gap with a fully managed browser service that lets AI agents drive these legacy interfaces … Read more
Mergers and acquisitions (M&A) teams face a persistent challenge: conducting thorough due diligence on multiple acquisition targets while maintaining speed and analytical rigor. Teams often spend weeks manually reviewing targets before identifying viable opportunities. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. It can … Read more
Your enterprise data lives in dozens of systems, but often the work happens in Microsoft 365. Amazon Quick bridges that gap as an AI assistant grounded in your data, now available directly inside Microsoft Word, Excel, PowerPoint, and Outlook. These extensions don’t require you to adopt a new application or change how you work. Instead, … Read more
Part 1 introduced granular cost attribution for Amazon Bedrock. This feature automatically traces every inference request back to the IAM principal that made the call. It showed how the new line_item_iam_principal column can give you per-user and per-application visibility. With optional cost allocation tags, you can also aggregate spend by team, project, or tenant using … Read more
This post is co-authored with OneAdvanced team Deploying AI agents on a United Kingdom (UK)-sovereign AWS architecture requires careful decisions about model hosting, data residency, and agent orchestration. OneAdvanced, a UK-based enterprise software provider serving over 10,000 customers, needed to deliver AI capabilities while making sure that no data would leave the UK. At the … Read more
This post is co-written with Patrick Duffy from Solv Labs and Houman Shadab from ICME Labs Solv Labs built an AI agent-payments workflow using Amazon Bedrock AgentCore payments, a capability of Amazon Bedrock AgentCore, governed by two layers: ORACLE (Solv’s policy engine) and ICME PreFlight for compliance verification. AgentCore payments provides the payment processing infrastructure. … Read more
Running large language model (LLM) inference at scale typically forces a KV cache trade-off: you either pay for oversized GPU instances to accommodate a growing KV cache, or you accept slow time-to-first-token (TTFT) as identical prompts get recomputed on every request. For teams deploying a broad catalog of publicly available foundation models (FMs), such as … Read more
Cyber defenders have never had more capability at their fingertips, and they have never needed it more. Frontier models can now reason across an entire code base, trace a vulnerability to its root cause, and propose a fix in minutes. Those same capabilities are available to adversaries. This is why the window between a vulnerability … Read more
This post was co-written by ONESTRUCTION, Inc. and Amazon Web Services Japan G.K. as part of GENIAC (Generative AI Accelerator Challenge) Phase 3, with technical advisory from the AWS Generative AI Innovation Center (GenAIIC). Building domain-specialized foundation models in data-scarce fields is hard. You need enough training data, specialized knowledge, and ways to verify your … Read more
This post is co-written with Ry Rainey and Graham Gibson from Pixieset. Photographers and artists are among the most skeptical audiences for generative AI. They have watched it threaten their craft and flood their industry with synthetic work. A 2025 MIT study found 95% of enterprise Generative AI pilots deliver zero measurable returns. Pixieset is … Read more
This post is co-written with Mark Himelfarb and Garrett Wilkerson from First Orion. First Orion’s engineering teams were shipping faster than quality assurance (QA) could test, until Amazon Nova Act transformed QA automation. As a branded communications company whose solutions reach hundreds of millions of phone calls across carriers in the US, Canada, UK, and … Read more
AI administrators deploying Claude Code and Claude Desktop across their workforce need centralized controls over authentication, model access, cost attribution, and spend enforcement. These controls reduce operational overhead and apply governance consistently at scale. Claude apps gateway provides a self-hosted governance layer between these applications and Amazon Bedrock or Claude Platform on AWS. Building on … Read more