Observability agents are fast. They query alerts, correlate logs with traces, and produce a root cause hypothesis in minutes. The part that still takes time is verification. You read the agent’s text summary, open your observability tools in a browser, navigate to the trace waterfall, check the service map to scope impact, and cross-reference what … Read more
Amazon Quick Desktop brings governed, AI-assisted reporting to the files your team already manages on Amazon FSx for NetApp ONTAP (FSx for ONTAP), cutting weekly report preparation from hours to minutes. Today, producing those reports takes hours of manual effort each week. Teams re-read the same documents, reformat metrics, and copy summaries into Slack. Leaders … Read more
Today, we are announcing new Ray capabilities on Amazon SageMaker HyperPod that integrate Ray with the HyperPod purpose-built infrastructure for foundation model training and serving. Ray is an open-source framework that data scientists use to scale distributed Python workloads across clusters of GPUs, from distributed training with Ray Train to model serving with Ray Serve. … Read more
Organizations across industries struggle with managing institutional knowledge, the collective wisdom and experience accumulated over years of operations. This “tribal knowledge” often disappears when key personnel leave, creating knowledge gaps that impact efficiency and innovation. Traditional documentation methods have proven inadequate, often resulting in outdated or inaccessible information when it’s needed most. In this post, … Read more
How AWS Agent Registry and the Agentic Resource Discovery (ARD) specification enable cross-environment discovery for your agents As organizations scale their use of artificial intelligence (AI) agents and tools, finding the right resource becomes the hard part. Teams build Model Context Protocol (MCP) servers, deploy agents, and create specialized tools, but without a central catalog, … Read more
At many restaurants, a large share of orders still arrive by phone, and those calls usually land on a staff member who is already taking care of customers at the counter. Callers wait on hold, orders get written down by hand, and busy periods make both worse. Adding an app or a website helps customers … Read more
As data collection and data generation accelerate, the gap between our ability to produce raw data and our capacity to standardize it continues to widen. Without automation, this gap becomes a critical bottleneck that delays analysis, complicates interpretation, and limits the global value of shared datasets. Metadata harmonization (standardizing labels, identifiers, and formats so datasets … Read more
Data engineering teams routinely spend weeks standing up a single new data source: writing ETL, hand-writing quality checks, updating semantic models, and validating compliance. The Agentic Data Operations Platform (ADOP) on AWS is designed to significantly accelerate that timeline. It’s a reference architecture built on Amazon Bedrock and your AI coding tool of choice. Specialized … Read more
In our conversations with customers over the past months, one pattern keeps recurring. Whether they work with coding agents, autonomous agents, or human-interactive ones, and regardless of workload maturity, we start with the same question: “Which AI agents have access to customer data, who granted it, and what would exposure look like if a credential … Read more
Input tokens sent to the foundation model (FM) on every call are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. Query-aware compression offers one way to reduce how many of them reach the model. Amazon Bedrock provides the foundation models and features to build RAG applications. RAG retrieval … Read more
Panasonic Avionics Corporation provides in-flight entertainment and connectivity (IFEC) systems across a large global fleet serving hundreds of airlines and billions of passengers annually. When a system issue affects passenger experience at this scale, engineers must diagnose the root cause quickly across thousands of unique deployment configurations. Doing this manually, correlating logs, metrics, and ticketing … Read more
This post is co-written with Chris Dickens from OpenAI. Amazon Bedrock now offers OpenAI GPT-5.6 models on Amazon Bedrock in more than 25 AWS Regions, with cross-Region inference. Three GPT-5.6 variants support cross-Region inference, Sol, Terra, and Luna, each tuned for a different balance of capability and cost. Cross-Region inference (CRIS) in Amazon Bedrock works … Read more
Healthcare, retail, and life sciences organizations generate massive quantities of operational data in cloud data warehouses like Snowflake. While these systems store and scale information efficiently, transforming that data into meaningful predictions remains a challenge. Traditional machine learning (ML) approaches require specialized teams, long development cycles, and heavy engineering support, creating delays and limiting experimentation … Read more
Part 1 covered the Snowflake database setup and established the foundational infrastructure for this no-code machine learning (ML) workflow. Part 2 of this blog series covers complete data preparation and model building workflow using Amazon SageMaker Canvas, demonstrating how to connect directly to Snowflake data sources, transform and prepare data using Data Wrangler’s visual transformations, … Read more
Part 1 covered the Snowflake database implementation setup and established the foundational infrastructure for our no-code machine learning (ML) workflow. Part 2 walked through the complete data preparation and model building workflow using Amazon SageMaker Canvas, demonstrating how to connect directly to Snowflake data sources, transform and prepare data using Data Wrangler visual transformations, and … Read more
AI agents can automate complex workflows but might take actions that don’t align with your organization’s policies or regulatory constraints if used without proper controls. To address this, we built Policy in Amazon Bedrock AgentCore so teams can implement controls that are applied across agents running in Amazon Bedrock AgentCore. This was recently expanded with … Read more
Scaling agentic AI across an enterprise requires architectural patterns that preserve flexibility while avoiding vendor lock-in. This post is Part 2 of our series on multi-agent systems at scale. In this post, we examine how machine learning (ML) teams operate agentic AI systems across a “multi-everything” environment of frameworks, models, and providers. We also cover … Read more
Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore starts with recognizing where large-scale migrations break down. Discovery consumes weeks per application. Engineers write infrastructure code from scratch for each workload. Post-migration operations devolve into reactive firefighting. Multiply those bottlenecks across over 300 applications and a fixed fiscal year deadline, and migration programs struggle … Read more
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