The Tech Radar - Issue #13 (February 2026)

Employee onboarding is rarely a linear process. It’s a complex web of dependencies that vary significantly based on an individual’s specific profile. For example, even a simple request for a laptop requires the system to cross-reference the employee’s role, function, and seniority level to determine whether they need a high-powered workstation or a standard mobile device.

Running large-scale inference models can involve significant operational toil, including cluster management and manual VM maintenance. One solution is to leverage a serverless compute platform to abstract away the underlying infrastructure. Today, we’re bringing the serverless experience to high-end inference with support for NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs on Cloud Run. Now in preview, you can deploy massive models like Gemma 3 27B or Llama 3.

Learn how Google's NAI framework uses AI to make technology more adaptive, inclusive and helpful for everyone.

A look at our 2026 Responsible AI Progress Report.

An overview of Google’s new global partnerships and funding announcements at the AI Impact Summit in India.

Lyria 3 is now available in the Gemini app. Create custom, high-quality 30-second tracks from text and images.

When AI agents make thousands of decisions a day, consistent performance isn't just a technical detail — it's a business requirement. Provisioned Throughput (PT) solves this by giving you reserved resources that guarantee capacity and predictable performance. To help you scale, we are updating PT on Vertex AI with three key improvements: Model diversity: Run the right model for the right job. Multimodal innovation: Process text, images, and video seamlessly.

We’ve updated Circle to Search so you can now explore multiple items in a single image.

Our latest image generation model offers advanced world knowledge, production-ready specs, subject consistency and more, all at Flash speed.

Nano Banana 2 (Gemini 3.1 Flash Image) delivers Pro-level intelligence and fidelity for all image applications.

Today, we’re entering a new and vibrant era of generative creativity with Nano Banana 2. What’s new: Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here .

AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, there would be no Google Cloud, as they are the ones building the future on our platform. In this regular round-up , we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories. For our latest edition, we learn how Google Cloud built an AI-powered training tool for US Ski & Snowboard ; a new data approach for Vodafone and Fastweb ;...

New alternatives, “understand” and “ask” buttons in Google Translate help you navigate the complexities of natural language.

Google is partnering with the Massachusetts AI Hub to provide every Baystater with no-cost access to Google’s AI training.

Editor’s note : Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more. In February, we’re giving developers more reasoning power with Gemini 3.1 Pro and Claude 4.6, and faster creative scaling with Nano Banana 2. We’re also opening up new training programs and step-by-step guides to help you tackle the hardest parts of the AI lifecycle, from capacity planning to mounting...

Welcome to the second Cloud CISO Perspectives for February 2026. Today, Royal Hansen, vice-president, Engineering, explains how we tackle today’s thorniest cybersecurity challenges. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog . If you’re reading this on the website and you’d like to receive the email version, you can subscribe here . aside_block ), ('btn_text', 'Visit the hub'), ('href', 'https://cloud.google.

In our conversations with business and policy leaders across Europe, we have listened closely to their concerns about sovereignty and European competitiveness. It is clear that they often find themselves caught in a paradox that Europe must choose between autonomy and growth. At Google Cloud, we believe that this is a false choice, and a premise that we challenge. Our position is rooted in partnership, choice, and security.

Welcome to the first Cloud CISO Perspectives for February 2026. Today, John Hultquist, chief analyst, Google Threat Intelligence Group, explains the research detailed in our newest AI Threat Tracker report. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog . If you’re reading this on the website and you’d like to receive the email version, you can subscribe here . aside_block ), ('btn_text', 'Visit the hub'), ('href', 'https://cloud.google.

The global conversation about our digital future goes beyond technology; it’s about architecting a prosperous, secure, and resilient economy where the digital services we rely on every day — from banking to healthcare to public administration — are built on a foundation of trust and operate according to local regulations. At Google Cloud, we believe that achieving this vision shouldn't require compromises.

Organizations that handle sensitive data in highly-regulated sectors often face a difficult choice: Build and manage physical hardware to meet strict compliance needs, or use cloud services that might not offer the specific level of isolation they require. These organizations, often in financial services, defense, healthcare, insurance, and government, require a key management service to provide cryptographic assurances that no one else — including their cloud provider — can access their keys.

Introduction Last week, Google Threat Intelligence Group (GTIG), Mandiant, and partners took action to disrupt a global espionage campaign targeting telecommunications and government organizations in dozens of nations across four continents. The threat actor, UNC2814, is a suspected People's Republic of China (PRC)-nexus cyber espionage group that GTIG has tracked since 2017.

Written by: Peter Ukhanov, Daniel Sislo, Nick Harbour, John Scarbrough, Fernando Tomlinson, Jr., Rich Reece Introduction Mandiant and Google Threat Intelligence Group (GTIG) have identified the zero-day exploitation of a high-risk vulnerability in Dell RecoverPoint for Virtual Machines , tracked as CVE-2026-22769 , with a CVSSv3.1 score of 10.0 . Analysis of incident response engagements revealed that UNC6201, a suspected PRC-nexus threat cluster, has exploited this flaw since at least mid-2024.

Introduction In the final quarter of 2025, Google Threat Intelligence Group (GTIG) observed threat actors increasingly integrating artificial intelligence (AI) to accelerate the attack lifecycle, achieving productivity gains in reconnaissance, social engineering, and malware development. This report serves as an update to our November 2025 findings regarding the advances in threat actor usage of AI tools.

Introduction In modern warfare, the front lines are no longer confined to the battlefield; they extend directly into the servers and supply chains of the industry that safeguards the nation. Today, the defense sector faces a relentless barrage of cyber operations conducted by state-sponsored actors and criminal groups alike. In recent years, Google Threat Intelligence Group (GTIG) has observed several distinct areas of focus in adversarial targeting of the defense industrial base (DIB).

Written by: Ross Inman, Adrian Hernandez Introduction North Korean threat actors continue to evolve their tradecraft to target the cryptocurrency and decentralized finance (DeFi) verticals. Mandiant recently investigated an intrusion targeting a FinTech entity within this sector, attributed to UNC1069 , a financially motivated threat actor active since at least 2018.

Learn how to use simple Gemini prompts to create a 2026 budget, find hidden savings and organize your spending.

For any organization that has invested in an internal developer platform, a question inevitably arises: Is it actually working? Simply tracking adoption rates won't tell you if your platform is truly delivering value to your developers. This was the challenge faced by John Lewis, a major UK retailer. In our previous articles (parts 1 and 2 ) we introduced the John Lewis Digital Platform (JLDP) and how it enabled dozens of product teams to build high-quality software rapidly to power www.

In part one of this article, Alex Moss from the John Lewis Partnership covered the metrics that they use to measure the value of their developer platform. Now, let's talk about a crucial aspect of any measurement strategy: choosing the right things to measure. It's easy to get lost in a sea of data or to focus on metrics that look impressive, but don't actually reflect the health of your platform or the experience of your developers.

As generative AI moves from experimentation to production, platform engineers face a universal challenge for inference serving: you need low latency, high throughput, and manageable costs. It is a difficult balance. Traffic patterns vary wildly, from complex coding tasks that require processing huge amounts of data, to quick, chatty conversations that demand instant replies. Standard infrastructure often struggles to handle both efficiently.

In a global economy, data is generated and stored all over the world. For a multinational corporation, customer data may reside close to headquarters in the US, while transaction logs are split between Europe and Asia, helping to meet performance, regulatory compliance, and data sovereignty needs. However, this creates a challenge: How do you get a single, unified view of your business if your data is spread across continents?

AI is poised to accelerate progress at an unprecedented rate — but only if we democratize access to it from the start. Today, Google is announcing new investments in digital infrastructure and skilling initiatives designed to increase connectivity, accelerate economic growth, and deepen resilience in India and around the world. Expanding AI connectivity across four continents Infrastructure is foundational to prevent the digital divide from becoming an AI divide.

Your AI agents are only as good as the data behind them. For enterprise teams building agentic AI, that's both the opportunity and the core question: How do you give Gemini and other AI models access to accurate, well-documented data when that data lives across dozens of systems, from modern cloud services to legacy mainframes? It's a question many organizations are actively working through. Gemini and other AI models depend on large volumes of AI-ready data to support agentic workflows.

For the second consecutive year Google (Spanner) has been recognized by Gartner in the Critical Capabilities for Cloud Database Management Systems for Operational Use Cases report, where it ranked #1 in the Lightweight Transactions Use Case. In our opinion, this is a testament to its efficiency, low latency, and high-performance consistency. It was also ranked #2 in the OLTP Transactions Use Case in the latest rankings.

Bringing data into BigQuery centralizes your information, but the real challenge is making that data accessible. Often, technical barriers separate the people with questions — from execs to analysts — from the answers they need. With the Conversational Analytics API , powered by Gemini, you no longer need intricate systems to get insights. The API is engineered to help you build context-aware agents that can understand natural language, query your BigQuery data, and deliver answers in text,...

An increasing number of customers are migrating to Spanner from legacy NoSQL environments like Apache Cassandra. The strategic drivers are evident: a markedly lower total cost of ownership (TCO), elastic scalability, and near-zero maintenance overhead. With the general availability of the native endpoint enabling Cassandra Query Language (CQL) APIs on Spanner, your existing Cassandra applications can now leverage Spanner’s enterprise foundation, featuring strong consistency, virtually limitless.

From the high-frequency trading floors of Wall Street to orchestrating cloud data centers, the ability to synchronize events with nanosecond accuracy is critical. Yet, achieving this level of temporal precision across thousands of interconnected devices in a modern data center is fraught with challenges like clock drift, network jitter, and path asymmetries.

With the dawn of the gen AI era, businesses are facing unprecedented opportunities for transformative products, demanding a strategic shift in their technology infrastructure. A few years ago, PayPal, a digital-native company serving hundreds of millions of customers, faced a significant challenge. After 25 years of success in expanding services and capabilities, we’d created complexity in our data analytics infrastructure.

February 23 - February 27 We introduced managed and remote MCP support for Google Cloud databases , including AlloyDB, Spanner, Cloud SQL, Bigtable and Firestore, to power the next generation of agents. This announcement extends the ability for AI models to plan, build, and solve complex problems, connecting to the database tools our customers leverage daily as the backbone of their work environment.

The divide between data in operational databases and analytical data lakehouses is disappearing fast. As businesses increasingly adopt zero ETL lakehouse architectures, the challenge shifts from simply storing data in an open data format such as Apache Iceberg to serving it with the low-latency performance and speed that modern applications and AI agents require.

In modern cloud architectures, APIs are the fundamental building blocks of applications. However, as organizations scale, these APIs often end up scattered across multiple gateways, teams, and platforms. This sprawl creates blind spots that hinder governance and reusability, but it creates an even bigger problem for the next generation of software: it can block the effective use of AI.

Enterprise architects often face a fundamental dilemma: choosing between developer agility and organizational control. Development teams need to move fast and deploy independent microservices without waiting for permission. Security and compliance teams need to be safe, and ensure that data flow is observable and governed by policies. That’s why we built Eventarc Advanced , a serverless eventing platform and the evolution of Eventarc Standard .

Machine Perception...

Algorithms & Theory...

Human-Computer Interaction and Visualization...

Climate & Sustainability...

Education Innovation...

Algorithms & Theory...

Generative AI...

Last week, my team met many developers at Developer Week in San Jose. My colleague, Vinicius Senger delivered a great keynote about renascent software—a new way of building and evolving applications where humans and AI collaborate as co-developers using Kiro . Other colleagues, Du’An Lightfoot, Elizabeth Fuentes, Laura Salinas, and Sandhya Subramani spoke about building and deploying production-ready AI agents.

Since we launched Amazon Nova customization in Amazon SageMaker AI at AWS NY Summit 2025, customers have been asking for the same capabilities with Amazon Nova as they do when they customize open weights models in Amazon SageMaker Inference . They also wanted have more control and flexibility in custom model inference over instance types, auto-scaling policies, context length, and concurrency settings that production workloads demand.

I joined AWS in 2021, and since then I’ve watched the Amazon Elastic Compute Cloud (Amazon EC2) instance family grow at a pace that still surprises me. From AWS Graviton-powered instances to specialized accelerated computing options, it feels like every few months there’s a new instance type landing that pushes performance boundaries further. As of February 2026, AWS offers over 1,160 Amazon EC2 instance types, and that number keeps climbing.

Here are the notable launches and updates from last week that can help you build, scale, and innovate on AWS. Last week’s launches Here are the launches that got my attention this week. Let’s start with news related to compute and networking infrastructure: Introducing Amazon EC2 C8id, M8id, and R8id instances: These new Amazon EC2 C8id, M8id, and R8id instances are powered by custom Intel Xeon 6 processors. These instances offer up to 43% higher performance and 3.

Over the past week, we passed Laba festival , a traditional marker in the Chinese calendar that signals the final stretch leading up to the Lunar New Year. For many in China, it’s a moment associated with reflection and preparation, wrapping up what the year has carried, and turning attention toward what lies ahead. Looking forward, next week also brings Lichun , the beginning of spring and the first of the 24 solar terms.

There’s a lot of excitement right now about AI enabling mainframe application modernization. Boards are paying attention. CIOs are getting asked for a plan. AI is a genuine accelerator for COBOL modernization but to get results, AI needs additional context that source code alone can’t provide.Here’s what we’ve learned working with 400+ enterprise customers: mainframe modernization has two very different halves.

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and your specific business needs when building applications that require domain-specific expertise, enforcing communication styles, optimizing for specialized tasks like code generation, financial reasoning, or ensuring compliance with industry regulations.

Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and other input factors, determines both computational requirements and costs. Longer contexts translate to higher expenses per inference request. This challenge has intensified as frontier models now support up to 10 million tokens to accommodate growing context demands from Retrieval Augmented Generation.

Organizations and individuals running multiple custom AI models, especially recent Mixture of Experts (MoE) model families, can face the challenge of paying for idle GPU capacity when the individual models don’t receive enough traffic to saturate a dedicated compute endpoint. To solve this problem, we have partnered with the vLLM community and developed an efficient solution for Multi-Low-Rank Adaptation (Multi-LoRA) serving of popular open-source MoE models like GPT-OSS or Qwen.

Large conferences and events generate overwhelming amounts of information—from hundreds of sessions and workshops to speaker profiles, venue maps, and constantly updating schedules. While basic AI assistants can answer simple questions about event logistics, most fail to deliver the personalized guidance and contextual awareness that attendees need to navigate complex, multi-day conferences effectively.

Managing large photo collections presents significant challenges for organizations and individuals. Traditional approaches rely on manual tagging, basic metadata, and folder-based organization, which can become impractical when dealing with thousands of images containing multiple people and complex relationships. Intelligent photo search systems address these challenges by combining computer vision, graph databases, and natural language processing to transform how we discover and organize...

The rapid advancement of artificial intelligence (AI) has created unprecedented demand for specialized models capable of complex reasoning tasks, particularly in competitive programming where models must generate functional code through algorithmic reasoning rather than pattern memorization. Reinforcement learning (RL) enables models to learn through trial and error by receiving rewards based on actual code execution, making it particularly well-suited for developing genuine problem-solving...

This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that are predefined, validated, and often strictly entered. This can include the schema for the output, or ways specific fields in the output should be mapped. Structured outputs are essential for applications that require consistency, validation, and seamless integration with downstream systems.

In November 2024, Amazon Web Services (AWS) was the first major cloud service provider to announce the ISO/IEC 42001 accredited certification for AI services, covering: Amazon Bedrock , Amazon Q Business , Amazon Textract , and Amazon Transcribe . In November 2025, AWS successfully completed its first surveillance audit for ISO 42001:2023 , Artificial Intelligence Management System with no findings. This demonstrates the continual commitment of AWS to responsible AI practices.

AI agents have traditionally faced three core limitations: they can’t retain learned information or operate autonomously beyond short periods, and they require constant supervision. AWS addresses these limitations with frontier agents—a new category of AI that performs complex reasoning, multi-step planning, and autonomous execution for hours or days.

Commercial AI services are enabling even unsophisticated threat actors to conduct cyberattacks at scale—a trend Amazon Threat Intelligence has been tracking closely. A recent investigation illustrates this shift: Amazon Threat Intelligence observed a Russian-speaking financially motivated threat actor leveraging multiple commercial generative AI services to compromise over 600 FortiGate devices across more than 55 countries from January 11 to February 18, 2026.

Enterprise customers face an unprecedented security landscape where sophisticated cyber threats use artificial intelligence to identify vulnerabilities, automate attacks, and evade detection at machine speed. Traditional perimeter-based security models are insufficient when adversaries can analyze millions of attack vectors in seconds and exploit zero-day vulnerabilities before patches are available.

We have got some exciting news to share: Microsoft Teams optimization for Amazon WorkSpaces is now generally available to all customers . If you’ve been looking for a way to make virtual meetings smoother and more reliable in your WorkSpaces environment, Microsoft Teams optimization provides this capability. From Public Preview to General Availability Back in September 2025, Microsoft launched the public preview of Microsoft Teams optimization for Amazon WorkSpaces .

Digital transformation in healthcare analytics demands solutions that balance sophisticated capabilities with simplified access. ZS , a global management consulting and technology firm and an AWS Advanced Consulting Partner, has applied its expertise in advanced analytics, AI, and scalable technology platforms to address this challenge. Serving a wide range of industries including pharmaceuticals, healthcare, and financial services, ZS transformed a standard virtual desktop setup into a...

Introduction Building Amazon Connect AI agents presents developers with a familiar challenge: tight timelines meet complex integration requirements. You need to connect multiple backend APIs, implement robust error handling, generate realistic test data, and debug multi-service interactions, all while maintaining code quality and consistency. A proof-of-concept that integrates 10-15 APIs can easily consume 2-3 weeks of development time, even for experienced teams.

Introduction When organizations embark on a contact center transformation journey, technology gets much of the focus. At the same time, it’s the people who determine whether a migration succeeds or falls short. Your agents, supervisors, and support staff are the heartbeat of your contact center, and their readiness to embrace change is as critical as any technical deployment plan.

Today is Groundhog Day, and just as Punxsutawney Phil emerges to forecast the season ahead, industry analysts and enterprise leaders are stepping forward with their predictions for customer experience (CX). Shadows or not, the real question is whether organizations will finally step into the spring of artificial intelligence (AI). As we move into 2026, the conversation around AI in CX is shifting from experimentation to execution.

Introduction Every customer interaction leaves digital traces that reveal preferences and needs. While businesses collect vast amounts of customer data, most struggle to move beyond basic personalization to understand the ‘why’ behind customer behavior. The key challenge isn’t gathering data—it’s converting these signals into real-time, actionable insights.

Educational technology (EdTech) providers face the challenge of maintaining seamless, personalized communication and presenting the right recommendations to their diverse stakeholders. This post explores how combining Amazon Web Services (AWS) End User Messaging and WhatsApp Business API with the advanced AI capabilities of Amazon Bedrock can transform educational engagement. In this post, we explore use cases that are reshaping the EdTech industry.

Starting today, Amazon CloudFront extends its mutual TLS (mTLS) capabilities to customer origins, which enables true end-to-end authentication throughout the entire connection path—from the viewers to the customer origins. CloudFront has supported viewer mTLS between viewers and CloudFront, so that customers can strongly authenticate clients before traffic ever enters their perimeter.

In this blog post, we will introduce the AWS Well-Architected Microsoft Workloads Lens , a comprehensive framework designed to help you build and operate Microsoft-based systems on Amazon Web Services (AWS) with confidence. Organizations continue to run Microsoft workloads on AWS to leverage the scalability, reliability, and innovation of the cloud while maintaining their investments in Microsoft technologies. Whether you’re running Windows Server, SQL Server, Active Directory (AD), .

Maintaining resiliency and operational clarity while keeping costs low is one of the key things our customers have to balance for their high-performance applications. As customers experience natural growth in capacity and performance needs, at some point they may hit the limits of the existing solutions and have to rethink their architecture. They can do so by moving to more expensive storage solutions, or by adding complexity to their applications by striping data across volumes, sharding it...

Migration assessments empower organizations to understand their on-premises infrastructure footprint and evaluate migration and modernization opportunities in the cloud. These assessments instill confidence in decision-makers and help organizations optimize their infrastructure, potentially reducing costs by up to 60% (Bain & Company, “ Rightsizing your way to the cloud “). AWS Transform assessments help eliminate the guesswork in the journey to the cloud.

Modern applications demand near-continuous availability, making database maintenance operations a critical challenge for development teams. Traditional approaches to database updates, patches, and upgrades often result in downtime, connection failures, and manual intervention requirements that disrupt business operations and impact user experience. Amazon Relational Database Service (Amazon RDS) blue/green deployments help to address the challenge of planned downtime by creating parallel...

This post was originally published in March 2024 and updated in February 2026. Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving overall customer experience. Apache Flink is a distributed computation framework that allows for stateful real-time data processing.

Introduction A medical imaging customer recently approached our team with a need to modernize their legacy Windows Presentation Foundation (WPF) Framework desktop applications into ASP.NET Core MVC Web applications. They wanted to use an agentic approach to speed up their modernization to achieve the up to 4x speed that other customers have realized with Amazon Q Developer .

If you’ve ever shopped on Amazon , you’ve used Your Orders . This feature maintains your complete order history dating back to 1995, so you can track and manage every purchase you’ve made. The order history search feature lets you find your past purchases by entering keywords in the search bar. Beyond just finding items, it provides a straightforward way to repurchase the same or similar items, saving you time and effort.

In this post, we show you how to migrate spatial (geospatial) data from self-managed PostgreSQL, Amazon RDS for PostgreSQL, or Amazon Aurora PostgreSQL-Compatible Edition to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition using AWS Database Migration Service (AWS DMS). Spatial data is useful for applications such as mapping, routing, asset tracking, and geographic visualization.

Today, we’re excited to announce the general availability of the collection groups feature for Amazon OpenSearch Serverless. With this feature you can reduce compute costs for multi-tenant workloads while creating secure tenant boundaries through per-tenant encryption, giving you the flexibility to balance cost efficiency with the exact level of isolation and security your applications requires.

Organizations are rapidly adopting Apache Iceberg for their data lakes because it supports petabyte-scale growth and performance with the flexibility to evolve schemas and partitions without costly rewrites. Its architecture enables modern data lake management via features like time travel and incremental processing. However, managing Iceberg datasets efficiently can become a challenge over time as data grows.

Manual scaling, patching, and maintenance tasks for traditional Amazon EC2 deployments consume valuable engineering time that could be better spent on innovation and feature development. Containerization offers a compelling solution to these challenges, providing improved resource utilization, faster deployment cycles, and reduced operational costs through infrastructure abstraction.

Amazon S3 is a highly scalable, secure, and durable object storage service that offers industry-leading performance and availability. It’s widely used to store a variety of unstructured data such as images, videos, backups, and logs. However, when uploading data to Amazon S3, especially large files or data accessed from different Amazon Web Services (AWS) Regions , choosing the right architecture pattern becomes crucial for optimizing performance and cost.

In alignment with our previous announcement in August 2025 and SDKs and Tools Maintenance Policy , version 4 of the AWS Tools for PowerShell (AWS Tools for PowerShell v4.x) will enter maintenance mode on March 1, 2026 and reach end-of-support on June 1, 2026. Beginning March 1, 2026, AWS Tools for PowerShell v4.x will enter maintenance mode and will only receive critical bug fixes and security updates.

Amazon Web Services (AWS) is announcing two new features for the AWS Command Line Interface (AWS CLI) v2 : structured error output and the “off” output format. Structured error output Errors returned from AWS service APIs often include useful details beyond the code and message—bucket names, validation reasons, resource IDs—that were previously hidden unless you used --debug . Now, you can see this error information directly in your error output. Starting with AWS CLI v2 version 2.34.

This post is cowritten by Julio Bando from Santander. Santander faced a significant technical challenge in managing an infrastructure that processes billions of daily transactions across more than 200 critical systems. The expansion into diverse financial services, including investment banking, wealth management, insurance, and payment solutions, had created unprecedented technological complexity, requiring a robust, agile, and scalable infrastructure solution. This raised two main issues.

This post is cowritten by Julius Blank from ProGlove. As software-as-a-service (SaaS) platforms grow, balancing speed of innovation with strong security and tenant data isolation becomes critical. While the same AWS Identity and Access Management (IAM) mechanisms secure both shared and dedicated environments, establishing a hard security boundary is often easier in an account-per-tenant model because the account itself becomes the isolation boundary.

Convera processes billions in cross-border payment volume yearly for businesses and financial institutions worldwide. As their platform grew, they needed a robust authorization system that could protect sensitive financial data while maintaining operational efficiency across their global network. In this post, we share how Convera used Amazon Verified Permissions to build a fine-grained authorization model for their API platform.

Background Amazon Key empowers customers to securely manage access to their homes and businesses through innovative solutions. Through a suite of consumer and business products, the Amazon Key team is transforming how customers receive deliveries and manage access to their spaces. Our In-Garage Delivery service offers a secure and convenient solution for receiving Amazon packages and groceries directly inside customers’ garages.

We’re introducing Strands Labs , a new Strands GitHub organization designed to give developers the ability to get hands-on with experimental, state-of-the-art approaches to agentic AI development. The Strands Agents SDK – available for both Python and TypeScript – has gained incredible traction in the developer community since we released it as open source in May of 2025.

Quantum computing promises breakthroughs in chemistry simulations. However, building scalable calculations on today’s noisy quantum hardware remains challenging. This blog introduces Kvantify Qrunch software on Amazon Braket. It enables advanced chemistry calculations using efficient hybrid quantum algorithms on current Noisy Intermediate-Scale Quantum (NISQ) devices. Chemistry is at the heart of several industries with profound economic and societal impact, such as pharmaceuticals and biotech.

We recently released v0.11 of the Qiskit-Braket provider , which brings more Qiskit features to Amazon Braket users, improves access to Braket backends through Qiskit, and also enables compilation on the Braket SDK or with OpenQASM Programs. With v0.11, the Qiskit-Braket provider now: Supports flexible compilation features for Braket using common Qiskit transpile functionality through the to_braket function Contains new BraketEstimator and BraketSampler primitives, which mirror routines found...

This post was contributed by Peter Eder, Aron Kerschbaumer, Jernej Rudi Finžgar, Raimel Medina, Martin Schuetz, Helmut Katzgraber, Sarah Braun, and Christian Mendl. Optimizing complex systems is a major challenge in science and industry. Many tasks – such as scheduling, routing, portfolio selection, or network design – can be written as combinatorial optimization problems that are simple to describe but very hard to solve, often trapping local search in difficult landscapes.

This post shows how quantum reservoir computing (QRC) can tackle machine learning challenges using Rydberg-atom quantum computers. Readers will learn how QRC works, see its performance on image classification and time series prediction tasks, and understand when it outperforms classical methods—particularly for small datasets in pharmaceutical research.

Securing card payment transactions while maintaining operational efficiency is a critical challenge for businesses of all sizes. The global network brand cards such as Visa, UnionPay, Mastercard, American Express, JCB and Discover/Diners Club generated $36.741 trillion in purchase volume worldwide in 2024 . As such, card payments trend is only expected to grow further and the need to keep payments secure grows with it.

Bioinformatics workflows are computationally expensive, often running for hours to days. When these workflows fail mid-execution or require iterative refinement, rerunning from scratch wastes significant time and compute resources. AWS HealthOmics call caching solves this challenge by intelligently saving and reusing completed task outputs, enabling workflows to resume from failure points rather than restarting entirely.

Introduction The network slicing market is accelerating as leading operators offer wide-scale services to enterprises and consumers. Network slicing is a 5G technology that partitions a single physical network into multiple, isolated virtual networks (slices), each customized for specific use cases like gaming, Extended Reality (XR), Internet of Things (IoT), and enterprise applications.

This blog post shows you how to transform days of manual financial analysis into seconds of comprehensive insights by building an Agentic GraphRAG solution for capital markets. You’ll see the architecture, graph schema design, and agent setup that lets business users ask complex questions in plain language. Capital markets firms track financial relationships across multiple data sources, from corporate ownership and supply chains to counterparty exposures spanning several degrees of separation.

Machine learning (ML) models operate directly in the critical path of ad delivery, influencing bidding, pricing, and campaign optimization under strict latency, reliability, and correctness requirements. These models are trained frequently on large volumes of historical auction data and produce deterministic artifacts that downstream serving systems rely on for consistent behavior in production.

At AWS re:Invent 2025, Nissan Motor Co., Ltd. (Nissan) announced the Nissan Scalable Open Software Platform , built on AWS, that accelerates Software-Defined Vehicle (SDV) development. Nissan began collaborating with AWS in 2023 to modernize its global engineering environment, reduce development bottlenecks and support next-generation vehicle innovation.

Details on OpenAI’s contract with the Department of War, outlining safety red lines, legal protections, and how AI systems will be deployed in classified environments.

OpenAI and Amazon announce a strategic partnership bringing OpenAI’s Frontier platform to AWS, expanding AI infrastructure, custom models, and enterprise AI agents.

Today we’re announcing $110B in new investment at a $730B pre money valuation. This includes $30B from SoftBank, $30B from NVIDIA, and $50B from Amazon.

Stateful Runtime for Agents in Amazon Bedrock brings persistent orchestration, memory, and secure execution to multi-step AI workflows powered by OpenAI.

Microsoft and OpenAI continue to work closely across research, engineering, and product development, building on years of deep collaboration and shared success.

OpenAI shares updates on its mental health safety work, including parental controls, trusted contacts, improved distress detection, and recent litigation developments.

OpenAI and Pacific Northwest National Laboratory introduce DraftNEPABench, a new benchmark evaluating how AI coding agents can accelerate federal permitting—showing potential to reduce NEPA drafting time by up to 15% and modernize infrastructure reviews.

OpenAI and Figma launch a new Codex integration that connects code and design, enabling teams to move between implementation and the Figma canvas to iterate and ship faster.

Our latest threat report examines how malicious actors combine AI models with websites and social platforms—and what it means for detection and defense.

OpenAI appoints Arvind KC as Chief People Officer to help scale the company, strengthen its culture, and lead how work evolves in the age of AI.

SWE-bench Verified is increasingly contaminated and mismeasures frontier coding progress. Our analysis shows flawed tests and training leakage. We recommend SWE-bench Pro.

OpenAI announces Frontier Alliance Partners to help enterprises move from AI pilots to production with secure, scalable agent deployments.

We share our AI model’s proof attempts for the First Proof math challenge, testing research-grade reasoning on expert-level problems.

OpenAI commits $7.5M to The Alignment Project to fund independent AI alignment research, strengthening global efforts to address AGI safety and security risks.

OpenAI for India expands AI access across the country—building local infrastructure, powering enterprises, and advancing workforce skills.

OpenAI and Paradigm introduce EVMbench, a benchmark evaluating AI agents’ ability to detect, patch, and exploit high-severity smart contract vulnerabilities.

A new preprint shows GPT-5.2 proposing a new formula for a gluon amplitude, later formally proved and verified by OpenAI and academic collaborators.

Introducing Lockdown Mode and Elevated Risk labels in ChatGPT to help organizations defend against prompt injection and AI-driven data exfiltration.

How OpenAI built a real-time access system combining rate limits, usage tracking, and credits to power continuous access to Sora and Codex.

GABRIEL is a new open-source toolkit from OpenAI that uses GPT to turn qualitative text and images into quantitative data, helping social scientists analyze research at scale.

Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users.

By Ryan Lopopolo, Member of the Technical Staff...

OpenAI for Government announces the deployment of a custom ChatGPT on GenAI.mil, bringing secure, safety-forward AI to U.S. defense teams.

OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.

OpenAI shares its approach to AI localization, showing how globally shared frontier models can be adapted to local languages, laws, and cultures without compromising safety.

An autonomous lab combining OpenAI’s GPT-5 with Ginkgo Bioworks’ cloud automation cut cell-free protein synthesis costs by 40% through closed-loop experimentation.

OpenAI introduces Trusted Access for Cyber, a trust-based framework that expands access to frontier cyber capabilities while strengthening safeguards against misuse.

OpenAI Frontier is an enterprise platform for building, deploying, and managing AI agents with shared context, onboarding, permissions, and governance.

A family shares how ChatGPT helped them prepare for critical cancer treatment decisions for their son alongside expert guidance from his doctors.

GPT-5.3-Codex is a Codex-native agent that pairs frontier coding performance with general reasoning to support long-horizon, real-world technical work.

GPT‑5.3-Codex is the most capable agentic coding model to date, combining the frontier coding performance of GPT‑5.2-Codex with the reasoning and professional knowledge capabilities of GPT‑5.2.

Learn how to embed the Codex agent using the Codex App Server, a bidirectional JSON-RPC API powering streaming progress, tool use, approvals, and diffs.

By focusing on people, not pilots, the Bundesliga club is scaling efficiency, creativity, and knowledge—without losing its football identity.

Discover the Sora feed philosophy—built to spark creativity, foster connections, and keep experiences safe with personalized recommendations, parental controls, and strong guardrails.

OpenAI and Snowflake partner in a $200M agreement to bring frontier intelligence into enterprise data, enabling AI agents and insights directly in Snowflake.

Introducing the Codex app for macOS—a command center for AI coding and software development with multiple agents, parallel workflows, and long-running tasks.

By Raksha Subramanyam and Vijay Singh. In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today, we feature an internal decision and automation platform, the Migration Intake and Processing Service (MIPS) . Engineers on the Cloud Economics and Capacity Management and Org Migration teams came together to build this platform, which enables Salesforce’s large-scale customer organization (org) migrations to Hyperforce, processing over..

In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today we spotlight Aastha Goyal, a Senior Manager of Software Engineering, whose team built FastTrack, a production-grade compliance automation platform for Salesforce’s mobile Apple App store and Google Play Store environments that delivers a 24× reduction in audit execution time.

By Yaheli Salina, Karthik Prabhu, and Omri Alon. In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Software Engineer Yaheli Salina. She and her Agentforce Speech Foundations team developed Speech Invocable Action — a new AI tool that standardizes repeatable actions, delivering sophisticated AI power throughout the Salesforce ecosystem, including secure, native speech automation housed within the platform trust..

By Shipra Shreyasi, Aniket Kumar, Manas Agarwal, and Pragya Kumari In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today we spotlight Shipra Shreyasi , a software engineering architect who directs the team enhancing natural-language-to-Flow creation within Agentforce. This empowers users to build production-ready Flow metadata from simple speech while managing automation at a scale surpassing 461 billion monthly executions.

By Irina Malkova and Alexander Smith. In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Irina Malkova, Vice President of Product and Success Data, who helped deliver the data foundation behind the Informatica Help Agent in just 24 days. Explore how the team met an ambitious deadline by refining project focus, converting 100,000 unstructured documents into searchable intelligence via Data 360 , and applying...

By Makarand Bhonsle, Christina Abraham, and Jayesh Govindarajan. In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today’s discussion features Makarand Bhonsle, a software engineering architect at Salesforce, whose team is developing Agentic Memory within Agentforce to provide durable, governable memory for enterprise agents at massive scale.

For decades, enterprise software has been an imperfect approximation of how businesses actually work. No matter how carefully a process is modeled in rules and workflows, reality has a way of drifting: customers behave unexpectedly, exceptions pile up, priorities shift, and edge cases become the norm. In those moments, people have filled the gap — applying judgment where the system’s model stops matching what’s happening on the ground.

By Andy Shah, Jeremy Klukan, Ben Drasin, Nikhil Pachpande, and Stuart Clark. In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. This edition features Andy Shah, Director of Software Engineering, who leads the development of Agentforce Enhanced Chat, a core customer-facing surface that brings Agentforce capabilities to the consumer web.

WALLDORF — SAP SE (NYSE: SAP) today announced that FC Bayern has migrated its on-premise systems to the cloud through the RISE with SAP journey. This strategic move is designed to accelerate innovation, strengthen data protection and future-proof the club’s digital operations. Access continuous innovations by modernizing your on-premises SAP ERP to SAP Business Suite Learn how Germany’s most successful football club and one of the world’s leading professional sports organizations, FC Bayern has.

SAP’s latest AI-powered innovation, SAP Logistics Management, is designed to empower localized and satellite business operations with innovative, agile tools tailored to their unique logistics challenges. Complementing SAP’s established logistics portfolio supporting large-scale supply chain operations, SAP Logistics Management brings innovative capabilities specifically tuned to the needs of smaller operations seeking efficiency, real-time visibility, and faster decision-making.

WALLDORF — SAP SE (NYSE: SAP) today announced that it has filed the SAP Annual Report on Form 20-F for the year ended December 31, 2025, with the U.S. Securities and Exchange Commission (SEC), and it is accessible online here . SAP also announced that the SAP Integrated Report 2025 is now available and accessible online at www.sapintegratedreport.com . You can access PDF versions of the SAP Integrated Report 2025 and the SAP Annual Report 2025 on Form 20-F at our Investor Relations website www.

With the rapid expansion of regulations concerning packaging and packaging waste, such as the extended producer responsibility (EPR), and plastic taxes, organizations face the pressing challenge of adapting quickly to remain compliant and competitive. Since 2021, SAP Responsible Design and Production has been SAP’s solution for calculating EPR fees across markets. At that time, the number of jurisdictions with installed mandatory EPR was around 60.

At a time when business-to-business commerce can be orchestrated with the touch of a screen, buying and selling across geographic borders has become increasingly frictionless. But what happens when the bill comes due, so to speak, from local jurisdictions? How may a business transact commerce globally while adhering to an emerging set of electronic-invoicing requirements that differ among countries?

Six teams are competing for the highest employee recognition at SAP: the Hasso Plattner Founders’ Award . Starting this year, the Hasso Plattner Founders’ Award comes with a modified, more focused approach. It now consists of two categories: “Scaling Innovation” and “Emerging Ideas.” Both reflect a different type of breakthrough thinking and the various ways in which innovation drives SAP’s success. This year’s award theme is AI .

WALLDORF — SAP SE (NYSE: SAP) today announced that the SAP Emarsys solution has been renamed to SAP Engagement Cloud reflecting SAP’s strategy to make engagement a core enterprise capability across the SAP portfolio. Deliver personalized, AI-driven engagement powered by SAP Business Data Cloud Learn how SAP is recognized in the 2026 Gartner® Magic Quadrant™ for Personalization Engines.

WALLDORF — The Supervisory Board and Executive Board of SAP SE (NYSE: SAP) recommend that shareholders approve a dividend of €2.50 per share for fiscal year 2025. This is an increase of €0.15, or 6.4% compared to the dividend paid for fiscal year 2024. If approved by shareholders and assuming the same level of treasury shares as of December 31, 2025, the total amount distributed in dividends would be approximately €2.919 billion (2024: €2.743 billion), representing a pay-out ratio of 40.

Six teams are competing for the highest employee recognition at SAP: the Hasso Plattner Founders’ Award . Starting this year, the Hasso Plattner Founders’ Award comes with a modified, more focused approach. It now consists of two categories: “Scaling Innovation” and “Emerging Ideas.” Both reflect a different type of breakthrough thinking and the various ways in which innovation drives SAP’s success. This year’s award theme is AI .

SAP has launched our new defense innovation hub , a dedicated environment designed to accelerate secure, mission-ready results across the defense ecosystem. The hub brings together startups, academia, industry leaders, and defense organizations to co-create solutions that strengthen digital sovereignty, operational resilience, and readiness in response to threats to safety and security.

The goal is clear for Royal Greenland and its more than 40 plants and factories along the coast of Greenland and Atlantic Canada: a more standardized, cloud‑based landscape with significantly lower complexity, and a technological foundation that can support future AI initiatives. SAP Cloud ERP: An out-of-the-box enterprise management solution Learn more Headquartered in Nuuk and 100% owned by the Government of Greenland, Royal Greenland is modernizing its SAP platform and moving from on premise.

The automotive industry has always been a bellwether for technological change. From mass production to lean manufacturing, from embedded software to connected vehicles, each wave of innovation has reshaped not just cars but entire ecosystems. Today, artificial intelligence is doing the same—quietly, decisively, and at scale. While much of the public conversation around AI in automotive focuses on autonomous driving or in-car experiences, the real transformation is unfolding behind the scenes,...

MUNICH — SAP SE (NYSE: SAP) today opened its defense innovation hub in Munich, Germany, underscoring its long-term commitment to strengthening digital readiness as a core element of modern defense capability. This comes at a time of growing geopolitical pressure, hybrid threats and rising demands for interoperability. Maintain mission readiness using intelligent solutions for ERP with advanced supply chain management Learn more The hub is launched as armed forces and security institutions face..

Meet your new process companion! Joule with SAP Signavio solutions is now generally available, helping users, analyze, and manage business processes using natural language. Better navigate constant change by turning business transformation into a core capability Learn how Joule is an AI solution that turns siloed data and tasks into intelligent, connected workflows that help improve decisions, speed up end-to-end processes, and create a unified AI experience across SAP and non-SAP systems.

Naturipe Farms knows berries. The grower-owned company produces sustainably grown berries including strawberries, raspberries, blackberries, blueberries, and cranberries, and delivers them worldwide every day. Naturipe has been an SAP customer since 2008, and while initially it was not very proactive with software upgrades, the company’s philosophy has changed over time.

SAP has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Personalization Engines for the seventh time in a row. We believe this recognition reflects the continued momentum of SAP Engagement Cloud in helping enterprises orchestrate real‑time, AI‑powered engagement at a global scale, connecting data, channels, and experiences to drive measurable business impact. This graphic was published by Gartner, Inc.

As we enter 2026, volatility and uncertainty have accelerated rather than eased, which puts additional pressure on global supply chains. At the same time, we are hearing from so many of our customers that technology is no longer just part of the supply chain story, but the solution to some of its toughest challenges. From geopolitical uncertainty to rising customer expectations, supply chain leaders are facing mounting pressure to deliver resilience, agility, and sustainability.

We are pleased to share that for the eighth consecutive year, Gartner has named SAP a Leader in its Magic Quadrant for Configure, Price, and Quote Application Suites. SAP CPQ enables organizations—however complex, across however many channels, and regardless of which CRM they run—to produce quick and accurate quotes, accommodating the most advanced configuration and pricing requirements, resulting in a better sales experience and faster sales cycles. This graphic was published by Gartner, Inc.

It’s no secret that organizations with fair and transparent pay practices earn greater employee trust and a stronger brand reputation. According to the latest Mercer Global Pay Transparency Report , when prospective candidates and current employees have clear visibility into how compensation decisions are made, organizations typically see increased engagement, enhanced productivity, and improved talent attraction and retention.

Sustainability rarely took center stage at Davos this year. Instead, it quietly delivered by playing an implicit and influential role in most conversations throughout the week. The major topics of geopolitical risk, artificial intelligence, and economic uncertainty consistently circled back to environmental exposure and long-term resilience, pointing to a broader shift: sustainability is becoming less of a separate agenda item and more an underlying consideration in enterprise risk and strategy.

Supply chain transformation today is a balancing act between ambition and execution. Leaders are being asked to deliver growth, improve resilience, adopt AI, and meet rising customer and regulatory expectations, all while keeping operations running. At NASCES25 , I moderated a roundtable with supply chain and transformation leaders from Toyota, L’Oréal, Owens Corning, Ecolab, American Airlines, and Bayer.