Multi-agent systems in production experience issues in ways that traditional monitoring misses. For example, the agent can’t invoke its foundation model (FM) and returns an empty response. This could be because of a missing AWS Identity and Access Management (IAM) permission on an agent’s execution role that doesn’t throw a 500 error. A supervisor agent … Read more
Organizations building generative AI applications usually compare models the same way: dollars per million tokens. It’s the number on every pricing page, so it becomes the number in every spreadsheet. But production workloads don’t buy tokens. They buy outcomes: a resolved support ticket, a completed research brief, a correct financial summary. Between the pricing page … Read more
As customers shift to interacting with digital services through AI hosts like ChatGPT and Claude, organizations need a way to make their services accessible across these applications with rich UI, not only plain text. They also need to do this without coupling to a single host. MCP Apps and Amazon Bedrock AgentCore provide exactly that. … Read more
When you build an application on top of a large language model (LLM), the prompt you send to the model typically has two parts. There’s a fixed part that sets up context (instructions, reference documents, conversation history) and a variable part that contains the actual user input. Take a customer service bot as an example. … Read more
When you deploy a large language model (LLM) for inference on Amazon SageMaker HyperPod, there’s a gap between when you request a pod and when it’s ready to serve traffic. This gap is dominated by two sequential downloads: the inference server container image from Amazon Elastic Container Registry (Amazon ECR), and the model weights from … Read more
Today we’re announcing the general availability of TwelveLabs Marengo Embed 3.0 as an embedding model in Amazon Bedrock Knowledge Bases. Video and media assets remain largely unsearchable by meaning. Teams in media, sports analytics, education, security, and retail need to find specific moments in hours of footage using natural language. An example query is “show … Read more
Your teams get an AI assistant that handles real work while your data stays in your environment and your conversations stay private Today, the Amazon Quick desktop application is generally available on macOS and Windows. We’re also adding a new activity feed to the mobile experience on iOS and Android that consolidates email, calendar, CRM, … Read more
Build an end-to-end Request for Information (RFI) questionnaire workflow using Amazon Quick Automate to solve a challenge organizations face at every scale. A typical enterprise might handle hundreds of RFI questionnaires each year, and each arrives as a complex, multi-tab workbook packed with hierarchical question sets, category metadata, and varied response types. The volume, variety, … Read more
A configurable, instruction-driven detector that runs on any large language model (LLM) managed on Amazon Bedrock, evaluated on five public PII corpora across nine LLM-based detectors, including the OpenAI PrivacyFilter. Fine-tuning a model on real-world text creates a personally identifiable information (PII) detection problem. Training corpora are full of PII: names, home addresses, email and … Read more
Multi-turn agents fail in ways that single-turn evaluation misses: one early mistake quietly corrupts every later turn. This post introduces the Agent Evaluation Metric (AEM), a decomposable, turn-level way to measure agent quality. We apply it to its first dimension, correctness. We show how AEM pinpoints the one turn that caused a failure and separates … Read more
This post is co-written with Petra Lafond, Product Manager, and Maarten Cardinaels, Tech Lead at AvioBook, a Thales Group Company. Airlines run on tight, cascading schedules, and accessing operational data holds the key to protecting them. During turnarounds, a few minutes of delay at one gate can ripple through an entire day of connections. It … Read more
On August 12, 2026, Alibaba’s Qwen team released Qwen3.8-2.4T-A95B. This is the first time a Qwen-Max-class model has been made available as open weights. With 2.4 trillion total parameters (95 billion activated per token), a hybrid linear-plus-full-attention architecture, and native context up to 262K tokens (extensible to 1M), Qwen3.8 targets the most demanding agentic and … Read more
A recap of the biggest Amazon Bedrock, AgentCore, and Strands updates from August 2026. At AWS, we have long focused on making foundational technologies accessible and providing the infrastructure needed to put them to work. Amazon Bedrock, used by more than 225,000 active customers, including over 80% of Fortune 100 companies, embodies that commitment by … Read more
Heurist uses Amazon Bedrock AgentCore to build AI-powered financial intelligence for retail investors. Its flagship product, Heurist Finance, brings several institutional-style workflows into one chat experience: it gathers market data, reads filings and news, runs deep research, builds and stress-tests portfolios, and monitors positions. Each answer reflects the user’s portfolio and preferences. Heurist’s goal is … Read more
TorchServe is no longer actively maintained. The official project notice states there are no planned updates, bug fixes, new features, or security patches, and that vulnerabilities might not be addressed. For teams that run model inference on TorchServe today, this means security patches stop and compatibility updates with newer versions of PyTorch and CUDA stop. … Read more
As Amazon Quick environments scale and new AI-powered capabilities expand what users can do, automating user-level custom permissions becomes critical to maintaining the principle of least privilege. To address this, with custom permissions in Quick, you can enforce fine-grained access control by toggling specific features on or off for individual users. For example, with custom … Read more
GPT-6 Astra from OpenAI brings greater depth and judgment to your most demanding tasks and runs on the Amazon Bedrock inference engine built for high performance, security, and scale. Organizations are already running AI agents that write code, analyze data, and automate complex workflows at production scale on Amazon Bedrock. GPT-6 Astra raises the potential … Read more
As AI systems take on more complex tasks, much of the industry’s progress has come from increasing model scale, training data, context length, and inference-time computation. Instead of externalizing reasoning work as a chain-of-thought (generating extra tokens sequentially and feeding them back into later steps), Pathway’s brain-inspired BDH (Dragon Hatchling) performs reasoning in latent space. … Read more
We are excited to announce feature-level writes for Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage machine learning (ML) features, the processed data used for training models and generating predictions. With the new UpdateRecord API, you can now update one or more feature values … Read more
Governing models across accounts is the natural next step once automatic model registration is in place. In Part 1 we introduced how managed MLflow on Amazon SageMaker AI synchronizes registered models into the SageMaker AI Model Registry. We walked through a single-account setup where AWS Identity and Access Management (IAM) condition keys separate the data … Read more
Automating model registration between MLflow and a model registry solves a gap that opens the moment a candidate model leaves experimentation. Data scientists track dozens of candidate runs in MLflow, while governance officers need one authoritative registry to validate, approve, and audit the models that reach production. Managed MLflow on Amazon SageMaker AI already synchronizes … Read more
Build a continuous integration and continuous delivery (CI/CD) quality gate that deploys an agent with role-based MCP tools, evaluates it, and blocks PRs when evaluation scores drop. You shipped an AI agent on Amazon Bedrock AgentCore runtime. It calls tools through an MCP server protected by OAuth. Now you want CI to tell you when … Read more
Choosing the right GPU instance for large language model (LLM) inference is one of the most impactful decisions you make when deploying generative AI at scale. A single generation jump can slash latency, increase throughput, and reduce cost-per-token. However, the real-world magnitude of those gains depends on model architecture, quantization format, and workload shape. In … Read more
This post was co-written by AWS and the HPE Zerto team. If you manage hybrid and multi-cloud infrastructures, you may already be turning to AI systems to assess health, investigate issues, and act on problems faster. HPE Zerto addressed this challenge by building an agentic troubleshooting system powered by Amazon Bedrock. HPE Zerto Software helps … Read more
DiDi partnered with AWS to build an intelligent contact center quality assurance (QA) system on Amazon Bedrock for its International Business Group’s Customer Experience (CX) department. The system covers Spanish and Portuguese across three business lines (ride-hailing, food delivery, and financial services) and migrates QA capabilities from an opaque third-party solution to a transparent, self-owned … Read more
This post shows how to deploy a multimodal WhatsApp ordering assistant built with Amazon Bedrock AgentCore and Amazon Nova 2. Many quick-service restaurants spread ordering across an app, a website, a phone line, and the counter. Each of those is a separate system to build and run. Each one also fragments the customer’s history, making … Read more
Memory lifecycle policies help long-running agents on Amazon Bedrock AgentCore stay effective by systematically managing what they remember and forget. Your agent generates memories from every conversation it conducts. If you don’t actively manage these memories, your agents will accumulate outdated context, which can degrade response quality and create compliance risks for your deployment. After … Read more
A Physical AI system, such as a robot or autonomous vehicle (AV) that translates real-world data into physical actions, can’t be built in a single training job. Instead, it takes a continuous pipeline: a loop of generating synthetic data, post-training perception and policy models, so the system understands its surroundings and can act, and evaluating … Read more
If you run foundation model (FM) workloads on Amazon SageMaker HyperPod, you know the work is rarely a single task. It is a chain of dependent ones. An infrastructure team creates the network and control plane, attaches accelerator capacity, and installs cluster dependencies in the right order. It also prepares storage and identity, keeps distributed … Read more
For customer service teams handling thousands of utility bills each month, accurately parsing and analyzing complex, multi-page documents is a persistent challenge. Inconsistent formats, dense tables, and varied layouts make it difficult to extract the right information quickly. This leads to delayed responses, billing errors, and frustrated customers. As document volumes grow, these inefficiencies compound, … Read more