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Amazon EventBridge relaunches event buses with a new enhanced Custom event bus that lets you build event-driven applications that decouple teams and can scale with your organization. Create a custom event bus, share it across accounts, and now teams can publish and consume events with strict ordering, open event formats, built-in retention, and support for advanced event transformations.
The EventBridge Custom event bus is a serverless event broker that enables you to create scalable event-driven applications by routing events between your applications, SaaS integrations, and AWS services. It can now be shared with one or more accounts through AWS Resource Access Manager allowing publishers to send events directly to the central event bus. With a new event publishing API, you can now publish events in popular JSON based event formats like CloudEvents and preserve their schema without modification. Custom event buses include 24 hours of built-in retention that can be extended for up to one year allowing you to recover from application errors or hydrate new components from historical data. A new Subscriber resource allows you to filter events and deliver them to over 250 AWS services. Now with native support for strictly ordered use cases, customers can ensure events are processed in the exact order they were received and new event evaluations allow automatic content based deduplication.
To get started, you can create and share the new Custom event bus using the AWS Management Console, AWS CLI, AWS SDKs, Serverless Agent skill, and AWS CloudFormation. To help you separate the existing Custom event bus experience from the new enhanced Custom event bus, we have renamed the existing bus to Custom event bus – classic. All existing APIs remain unchanged.
The enhanced Custom event bus is available at launch in fourteen AWS Regions: United States (N. Virginia, Ohio, Oregon), Europe (Ireland, Frankfurt, Stockholm, Spain), and Asia Pacific (Tokyo, Singapore, Sydney, Malaysia, Thailand, Mumbai, Hong Kong). We have introduced a new pricing model that charges for data transferred rather than events, and rewards you with lower costs as your workloads scale. To learn more, visit Amazon EventBridge.
To learn more, see the AWS News blog, the EventBridge user guide, and the EventBridge product page, or download the agent skills.
Quelle: aws.amazon.com

25. September 2026

da Agency

How growing Latin American midsize businesses are building in the AI era

Latin America’s small and medium-sized businesses are the heartbeat of the region’s economy — accounting for more than 60% of total employment in the region, according to United Nations estimates. And just like their enterprise peers, everywhere you look, ambitious teams are moving fast to embrace AI.
Many have already transitioned from experimenting with generative tools and agentic workflows to using them every day to work smarter, save time, and deliver exceptional customer experiences. These growing businesses are particularly focused on maximizing the benefit they get from their investments in AI, whether that’s using a fast, low-cost model to summarize daily emails or deploying an advanced model for complex data analysis, teams can match the right AI capability to their exact task and budget.
It’s this range of options, and a familiarity with the broader suite of Google business, media, and advertising tools that has led many SMBs to choose Google Cloud, and Gemini Enterprise in particular, as their AI platform of choice. By doing so, they’re able to build custom AI agents, streamline daily tasks and paperwork, and offer customers instant support with the speed and reach needed to compete on a global scale.
With our unique front row seat, we’ve seen the benefit SMBs are getting from leveraging Gemini Enterprise, not only for generative AI, but as a catalyst for adopting other essential cloud tools like Google Kubernetes Engine and BigQuery for complete end-to-end modernization. The number of Latin American-based small and medium businesses using Google Cloud AI tools has grown 8x year-over-year and the number of Brazil based small and medium businesses using Google Cloud AI tools has grown year-over-year. This rapid adoption spans our Gemini models, Gemini Enterprise, and core Cloud infrastructure, and are helping businesses to:

Roll out better customer support systems to help escalate and resolve customer support calls more quickly.

Automate repetitive actions in areas like payroll and accounting.

Help more employees understand and leverage data at work — even those not trained as data analysts.

Rapidly create and implement new designs for marketing collateral.

Help more people build their own AI agents to help them in their everyday jobs.

As we head into today’s Google Cloud Summit in Brazil, we were proud to showcase nearly 20 of our newest Latin American SMB customers using Google AI to reduce busywork, serve their customers faster, and grow their businesses.
Announcing new Latin American customers putting Google AI to work

AdGoat, an Argentina-based adtech company processing more than 10 billion annual ad requests across more than 100 global websites. It uses Cloud Run, the Gemini API, and Gemini Enterprise to automate content analysis, ad bidding, and audience targeting to help e-commerce brands drive higher campaign returns.

Angelus, a Brazilian dental and healthcare manufacturing company, uses Gemini Enterprise to streamline project management across its research and development department. This enables its teams to automatically pull technical project data into pre-approved templates aligned with the company’s brand identity and regulatory requirements.

BunkerDB, a marketing science company operating across Latin America, uses Gemini Enterprise, Cloud Run, and Cloud Storage to power an AI platform that organizes marketing assets, checks brand compliance, generates or adapts multimodal content, and predicts ad performance before launch. All of this helps it reduce creative turnaround times from weeks to hours and cut cost per lead by up to 25%.

Caffeine Army, a Brazilian wellness and high-performance company that connects people with solutions in nutrition, sports, and well-being, deployed BigQuery and Gemini Enterprise on Google Cloud to unify customer purchase insights, enabling faster creative campaign turnarounds and boosting team productivity across the organization.

Convert, a Brazilian marketing and analytics provider, uses Looker, BigQuery, and Cloud Run to power five specialized AI agents that answer complex business questions in natural language, speeding up report deliveries by 65% and reducing operational costs by 32%.

Growth Digital, a Google Ad sales rep operating across 13 Latin American countries, used BigQuery and Gemini Enterprise to build over 113 AI agents, enabling teams to build proposals 5x faster, cut campaign reporting time by 80%, and reduce financial error rates to under 0.01%.

GrupoTusMaquinas.com, an equipment management platform based in Chile, deployed Google Cloud AI tools and Gemini models to create digital tracking profiles for trucks and machinery, allowing businesses to query fleet status in plain language and manage vehicles regardless of brand or location.

HealthAtom, a healthcare technology company, uses the Gemini API, Firestore, and Cloud Functions to power AI assistants across its clinical platforms, automating appointment scheduling and medical record reviews while supporting 80 million annual patient interactions.

KLog.co, a Chilean logistics technology company digitizing freight forwarding across Latin America, uses Gemini Enterprise, BigQuery, and Google Workspace to automate cargo tracking and shipping paperwork, cutting manual data entry errors by over 90% and increasing document processing capacity tenfold.

NEEOH, a leading Brazilian out-of-home advertising communication platform, uses Gemini Enterprise to standardize secure AI usage across its organization, enabling teams to generate campaign copy and build pitch proposals faster while keeping corporate client data secure.

Luxia Agro, an Argentinian foreign trade supplier of crop protection products, uses Gemini 3.5 Flash and Gemini Enterprise to automatically pull key details from complicated shipping emails and update their central business systems. This allows it to automate 80% of foreign trade operations and cut manual processing errors in half.

Macal, a Chilean auction company, uses the Gemini Enterprise, Cloud Run, BigQuery, and Security Command Center to automatically verify property records and modernize its technology systems, cutting software development times from weeks to days and lowering infrastructure costs by up to 30%.

Ninecon, a Brazilian tech consulting firm, deployed Gemini Enterprise to integrate AI directly into employee workflows, allowing managers to track usage patterns and optimize project turnaround times with real-time insights.

Nova Gestões, a customer service and operations provider in Brazil, uses Cloud Speech-to-Text and Gemini Enterprise to translate and analyze 100% of customer calls in real time, reducing post-call manual data entry and boosting team productivity by 30%.

Romi, a Brazilian industrial machinery manufacturer, uses the Gemini API and Gemini Enterprise to power an interactive chat assistant directly on CNC machine HMI (human machine iInterface), giving factory operators instant answers grounded in official manuals and generating QR codes for step-by-step instructional videos.

Supermercados El Dorado, a leading supermarket chain in Uruguay, leverages Google Compute Engine and Gemini Enterprise to modernize legacy testing infrastructure and connect custom AI agents within their daily workflows, boosting team productivity across departments.

Tryvia, a Brazilian IT and business solutions provider, uses Google Cloud, Looker, and Gemini Enterprise to move off legacy physical servers, giving teams real-time reporting dashboards and AI tools that speed up software development and daily tasks.

Via Cristais, a major highway operator in Brazil, leverages Google Contact Center as a Service to speed up emergency routing for highway accidents, reducing caller wait times, improving driver satisfaction, and mitigating the impact of call center staff turnover.

WeSpeak, an AI conversational platform for the hospitality industry in Latin America, uses Cloud Run, Gemini Pro, and Gemini Flash to automate end-to-end guest interactions across messaging channels like WhatsApp and Instagram. This has helped it achieve an 85% resolution rate and a 2x increase in overall sales volume for hotel clients.

Helping your team build AI skills
To help growing teams get the absolute most out of AI, we’ve created easy, no-cost learning programs that anyone can use:

Programs for small and medium businesses: Explore beginner-friendly training paths or join specialized programs to learn how to build custom AI assistants for your day-to-day work.

Google skills for organizations: Access thousands of free, on-demand AI courses and hands-on practice labs designed by experts at Google Cloud and Google DeepMind.

Get certified: Help your staff gain industry-recognized AI certificates through guided courses, expert mentoring, and skill badges.

By offering easy-to-use tools and free training — from everyday office apps in Workspace to advanced AI on Google Cloud — Google is here to help Latin American businesses thrive today and in the future.
Quelle: Google Cloud Platform

25. September 2026

da Agency

Claude Opus 5.5 comes to Microsoft Foundry for long-running coding and knowledge work

AI models are increasingly taking on work that extends far beyond a single prompt: building a feature across a codebase, investigating a complex issue, synthesizing hundreds of pages of information, or working through a multi-step business process.

As that work gets longer, raw intelligence is only part of what matters. The model also needs to stay focused, make good decisions along the way, communicate what it is doing, and produce work that people can quickly review and use. Today, Claude Opus 5.5 is available in Microsoft Foundry, bringing Anthropic’s most capable Opus model to developers and enterprises building AI applications and agents.

Claude Opus 5.5 is designed for everyday complex work. It advances Opus 5 across agentic coding, knowledge work, and long-running tasks while making it easier for people to understand what the model did, what it found, and what it needs next. Claude Opus 5.5 also does more with fewer tokens. Lower per-token prices and much cheaper cache reads stack on top of the efficiency gains.

Built for work that takes time

Writing a function is one thing. Building a feature that touches multiple services, tracing a production issue across a large repository, or carrying a task from investigation through implementation and validation is something else entirely. Claude Opus 5.5 is designed for these longer-running workflows.

For software development, it can work through long-running coding tasks such as building features across a codebase, debugging, refactoring, and reviewing code. It finds the root cause before changing anything, checks its work as it goes, and explains its changes in plain language, so engineers can review and trust them quickly.

That combination becomes particularly valuable when developers use models through agentic coding environments, where a session may involve dozens of steps and run for an extended period of time. The same applies beyond software development.

For knowledge workers, Claude Opus 5.5 can bring together information from multiple sources, work through long documents and spreadsheets, perform analysis, and help create artifacts such as memos, reports, and presentations. Compared with Opus 5, it produces outputs that require less editing before they are ready to share.

An AI model that communicates more like a teammate

As agents take on more autonomous work, another challenge emerges: keeping the human in the loop without overwhelming them.

An agent that performs 50 steps should not require someone to inspect 50 steps to understand whether the work was successful. Claude Opus 5.5 introduces improvements to agentic communication designed to make long-running work easier to follow. As it works, the model can surface the information that matters most:

What it did

What it found

What decisions it made

Where it needs input from the user

What happened at the end of a long-running task

The goal is simple: spend less time decoding what the model did and more time using the result. This matters particularly for enterprise agents, where users need to understand not only the final answer but also when an agent needs clarification, encounters a constraint, or reaches a decision point that requires human judgment.

Adpative thinking

Claude Opus 5.5 uses adaptive thinking, automatically determining how much reasoning a task requires. Rather than turning thinking on or off or manually specifying a thinking-token budget, developers use effort to influence how much work the model should put into a request. This allows the model to adapt its reasoning to the task at hand—from relatively straightforward requests to complex problems that require deeper analysis.

For developers building agents, this can reduce the amount of application logic needed to decide when and how a model should reason.

Designed for long-running agent architectures

Long-running agents create challenges beyond model intelligence. Conversations can exceed context limits. Tools available to an agent can change. Applications may need to compact earlier context while preserving the model’s understanding of the work already completed.

Alongside Claude Opus 5.5, Anthropic is introducing beta API capabilities designed for these scenarios, including asynchronous compaction, keep-tail compaction, and changing tools during a conversation while preserving thinking and prompt caching. These capabilities can help agent developers maintain continuity across longer tasks without rebuilding the state of the application every time context or available tools change.

Combined with Microsoft Foundry, developers can use Claude Opus 5.5 as part of broader agent systems that connect models with enterprise data, tools, evaluation, and operational workflows.

Expanded safeguards for more capable models

As model capabilities increase, Anthropic is also expanding the safeguards applied to Claude Opus 5.5.

Claude Opus 5.5 is the first Opus model to use safety classifiers like those introduced with Claude Fable 5.1 in areas including cybersecurity, biology, AI development, and distillation.

For common developer, educational, and knowledge-work scenarios, customers can continue using the model for tasks such as identifying software vulnerabilities or learning about biological concepts. Certain requests that Anthropic identifies as higher-risk or dual-use may be handled by another Claude model with the appropriate safeguards.

This reflects an increasingly important part of deploying more capable models: advancing what models can do while applying safeguards appropriate to the capabilities they introduce.

Pricing

ModelDeployment OffersInput/M TokensOutput/M TokensAvailabilityClaude Opus 5.5Global Standard, US DataZone$4Cache Hit – $0.20Cache Write – $5Cache Write (1 hr) – $8$20GA, Hosted on AzureClaude Opus 5.5 (Long Context)Global Standard, US DataZone$4Cache Hit – $0.20Cache Write – $5Cache Write (1 hr) – $8$20GA, Hosted on Azure

Build with Claude Opus 5.5 in Microsoft Foundry

Choosing a model is only the beginning of putting AI into production. Microsoft Foundry gives developers a unified place to discover models, build and evaluate AI applications and agents, connect them with enterprise data and tools, and operate those systems in production. As models become capable of taking on more complete units of work, the question is shifting from Can the model answer this prompt? to Can I trust it to carry the work forward? Claude Opus 5.5 represents another step in that direction: stronger performance on complex work, more adaptive reasoning, and clearer communication between people and the AI systems working alongside them.

Claude Opus 5.5 is available today in Microsoft Foundry.

Updated Sep 22, 2026

Version 2.0
The post Claude Opus 5.5 comes to Microsoft Foundry for long-running coding and knowledge work appeared first on Microsoft Azure Blog.
Quelle: Azure

25. September 2026

da Agency

GPT-6 Astra, Sol, and Luna: For production agents in Microsoft Foundry

Today, we are expanding our GPT-6 series by welcoming GPT-6 Sol and GPT-6 Luna to our generally available lineup in Microsoft Foundry. Building on the exceptional customer momentum of GPT-5.6 Sol and GPT-6 Astra, this launch continues our work to deliver transformative capabilities in Microsoft Foundry that produce less noise and are more capable at completing full tasks with agents.

Explore GPT models in Foundry today

Astra brings advanced reasoning, software engineering and computer use to demanding work that requires both judgment and action. Azure customers report a step-change in capabilities, and strong cost-to-performance with the model using fewer, higher-value tokens to drive agents.

Completing the lineup, GPT-6 Sol is excellent for general-purpose use, while Luna brings efficient intelligence to high-volume data and preparatory work.

Put the right intelligence behind every agent

The right model for a job should be determined through evaluations: an agent handling a complex business decision and one routing routine requests have different needs. Microsoft recommends customers start with GPT-6 Astra for demanding work. For higher-volume workloads, GPT-6 Sol and Luna carry that progress forward, giving you a complementary choice built for production and scale.

GPT-6 Sol for production AI agents and complex workflows

GPT-6 Sol, and its proven predecessor—GPT-5.6 Sol—offer slightly more cost-effective intelligence with frontier efficiency. They support enterprise agents, coding and complex knowledge work, including reasoning across multiple steps, long-context analysis, and workflows that use tools. For teams evaluating their next production workload or migrating off a legacy model, Sol is a strong starting point.

GPT-6 Luna for efficient, high-volume AI workloads

GPT-6 Luna is Sol’s smaller, faster sibling, built for high-volume work. Use it for extraction, summarization, request routing, and routine customer interactions. Reserve deeper reasoning for the steps that need it, rather than applying the same model to every task.

As the GPT-6 lineup expands, the opportunity is not simply to choose a newer model, but to improve what your agents can accomplish while saving money. Customers should look beyond pricing per token and seek to understand cost per task, which is a better measure for understanding the ROI of AI.

The accompanying chart illustrates why enterprise customers on Microsoft Foundry are switching to GPT-5.6 Sol and the latest GPT-6 offerings.

Foundry brings evaluation and monitoring together so teams can make those decisions with evidence. The real measure of that progress is what customers can do in production, which is why Foundry has always encouraged model choice and an open, interoperable stack.

The Foundry advantage, in customers’ words

Access to frontier models is only the starting point. Foundry pairs GPT-6 intelligence with the breadth of deployment options enterprise production demands. Today, Standard deployment is available for Astra, Sol and Luna across all 28 Global regions, and US and EU Data Zones; Provisioned Throughput for Astra and Sol across Global regions and US and EU Data Zones; and Priority Processing for Sol across Global regions and US Data Zones. The breadth and performance of Azure is why OpenAI continues to launch first on Azure, and why sophisticated customers like Manus choose Foundry.

Azure OpenAI models provide a core layer of intelligence powering Manus. Through Azure, we reliably integrate advanced models into our agentic workflows, enabling Manus to understand user intent, plan tasks, and execute complex work. Responsive Microsoft technical support and rapid access to new model capabilities help us iterate quickly and deliver a leading, reliable AI experience for our users.
—Tao Zhang, Co-Founder & Product Partner, Manus

For customers getting started with AI on Azure: choose Global for flexible, pay-per-token capacity, or supported Data Zone deployments for processing-location requirements. Priority Processing is a priority lane for responsive, pay-as-you-go experiences, with Provisioned Throughput providing reserved capacity and superior latency for critical production demand. Match the serving option to the workload, from interactive agents to high-throughput business processes.

That is the Foundry advantage: not just frontier intelligence, but the platform to put it to work. Teams can match each workload to the right model, deployment option, and controls, balancing capability, responsiveness, and cost as adoption grows. By bringing these choices together on Azure, Foundry helps customers focus on delivering business value, with the operational foundation to move from a promising agent to production at scale.

Our customers work in domains where getting an answer isn’t enough, it has to be the right answer, and it has to hold up to scrutiny. The latest Azure OpenAI frontier models reason through a problem in steps we can follow, which is what makes it viable for the research and compliance workflows our professionals depend on. Building on Microsoft Foundry lets us take those agentic workflows into production on infrastructure and services that already meet our governance, data residency, and security obligations.
—Brian Diffin, CTO of Wolters Kluwer Tax & Accounting

GPT-6 pricing and deployment options**

ModelDeploymentContext LengthPricing (USD $/million tokens)InputCached InputCached WritesOutputGPT-6 AstraGlobal StandardShort context$10.00$1.00$12.50$50.00Long context$20.00$2.00$25.00$75.00Data Zone Standard (US)Short context$11.00$1.10$13.75$55.00Long context$22.00$2.20$27.50$82.50Data Zone Standard (EU)Short context$12.00$1.20$15.00$60.00Long context$24.00$2.40$30.00$90.00GPT-6 SolGlobal StandardShort context$2.00$0.20$2.50$10.00Long context$4.00$0.40$5.00$15.00Data Zone Standard (US)Short context$2.20$0.22$2.75$11.00Long context$4.40$0.44$5.50$16.50Data Zone Standard (EU)Short context$2.40$0.24$3.00$12.00Long context$4.80$0.48$6.00$18.00GPT-6 LunaGlobal StandardShort context$0.10$0.01$0.125$0.50Long context$0.20$0.02$0.25$0.75Data Zone Standard (US)Short context$0.11$0.011$0.1375$0.55Long context$0.22$0.022$0.275$0.825Data Zone Standard (EU)Short context$0.12$0.012$0.15$0.60Long context$0.24$0.024$0.30$0.90

**Pricing for both Provisioned Throughput and Priority Processing varies by deployment type. For each offer, U.S. Data Zone is priced at a 10% premium to Global. For current rates and terms, see the Azure OpenAI pricing page.

Build safer AI agents with Microsoft Foundry

GPT-6 models running on Azure have multiple layers of safety and security built directly into the model and around it. At the core, the model itself carries the alignment and safety training built in, while the prompts and outputs around it are protected by content filters and guardrails that govern what the agent can say. Beyond that, tool calls and responses are protected by controls and prompt injection mitigation that govern what the agent can do, and identity and access are protected by enterprise policies that govern what it can reach.

Foundry helps teams continuously strengthen safety layers as risks evolve. It applies guardrails at key checkpoints, including prompts, outputs, tool calls, and tool responses. Identity and access controls govern what agents can do and reach. Microsoft Purview applies enterprise data policies. Evaluation, tracing, and monitoring give teams the evidence to optimize those controls over time, with human checkpoints at every phase.

Move to GPT-6. Build your next generation of agents.

Build your next agentic workloads in Microsoft Foundry. Start with GPT-6 Astra for demanding reasoning, Sol for general production use, and scale high-volume tasks with GPT-6 Luna. For customers of legacy models, we recommend evaluating an upgrade to GPT-5.6 Sol and above.

Your next agent needs more than a powerful model. Foundry brings an open intelligence stack, deployment flexibility, and Azure enterprise controls together so you can build with confidence and scale from your first workload to production.

Start building in Foundry today

Access GPT-6 models, evaluate the right fit for your workload, and scale from experimentation to production.

Begin here

The post GPT-6 Astra, Sol, and Luna: For production agents in Microsoft Foundry appeared first on Microsoft Azure Blog.
Quelle: Azure