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The real AI competition is now happening in enterprise stacks.AI_Distilled #128: What’s New in AI This WeekJoin the Next Batch of AI Agent Builders (Cohort 3)Over 120+ engineers have already built AI Agents through Cohorts 1 & 2.In Cohort 3, you could be one of them.It’s a live, hands-on cohort built for engineers who want to stop watching demos and start shipping real AI agents.In just one weekend, you’ll build systems that: Reason → Call Tools → Take Actions → Run WorkflowsUsing LangChain, AutoGen & CrewAI.With live coding and mentor support from experts at Microsoft & Google.Exclusive for Our Newsletter ReadersAs part of our NL community, you get 40% off your seat.Use code: AGENT40 (Valid for a limited time)If AI agents are on your 2025 roadmap, this is your moment.Secure Your Discounted Seat HereMiss this cohort, and you will have to wait for the next one.This week, AI moved decisively from experimentation to execution. OpenAI introduced Frontier, signaling a shift toward managed enterprise agent fleets. Anthropic expanded Claude’s real-world capabilities. Governments doubled down on sovereign compute. Meanwhile, new benchmarks like AIRS-Bench are pushing scientific evaluation of autonomous systems. The race is no longer about model size — it’s about infrastructure, orchestration, and who can operationalize intelligence at scale.LLM Expert Insights,PacktLATEST DEVELOPMENT🧠 Enterprise AI & Agent OpsOpenAI launches Frontier — the enterprise AI agent platformOpenAI unveiled Frontier, an enterprise-grade platform for building, deploying, and managing autonomous AI agents that operate across business systems with shared context, onboarding, and governance — shifting the battleground from standalone models to managed agent fleets integrated with internal data sources and workflows.🔁 Competitive Model WarsAnthropic’s Claude Opus 4.6 expands enterprise capabilitiesAnthropic released Claude Opus 4.6, targeting complex enterprise tasks like deep reasoning, analytics, and coding, claiming superior benchmarks in knowledge-intensive work — a move that underscores how model evolution is increasingly tied to real-world utility rather than raw size alone.⚙️ Tools, Frameworks & Agentic SoftwareOpenAI & Anthropic rivalry spills over into tooling and deploymentBeyond models, competition is playing out in agentic tool ecosystems, with dueling releases and strategic positioning that aim to blur lines between productivity coding, agent workflows, and software lifecycle support — a sign that “agent stacks” are now core to enterprise AI competitiveness.📈 AI Infrastructure & Compute StrategyCanada pushes sovereign compute with multi-billion strategyCanada reaffirmed its Sovereign AI Compute Strategy, committing major public and commercial infrastructure investments to ensure domestic access to AI compute, supercomputing resources, and affordable capacity for innovators — a move poised to shape North American research and commercialization landscapes.🔬 Research & BenchmarksAIRS-Bench accelerates scientific research agent evaluationA new benchmark suite, AIRS-Bench, has been introduced to assess AI agents’ scientific reasoning across interdisciplinary research tasks, revealing where current agents outperform humans and where significant gaps remain — a useful tool for rigorous evaluation of agentic systems in research workflows.📈EXPERT INSIGHTSUnlocking Data with Generative AI and RAGThis week’s excerpt comes from Unlocking Data with Generative AI and RAG (2nd Edition) by Keith Bourne, an AI engineer and founder of Memriq AI. Drawing on a decade of experience building production-scale ML systems for companies like Johnson & Johnson, Bourne unpacks how Retrieval-Augmented Generation (RAG) is reshaping the way organizations use data. The book goes beyond theory, offering hands-on guidance for integrating RAG with generative AI to build faster, smarter, and more adaptive systems.RAG for automated reportingCompanies that use RAG incombination with data analysis and reporting through its automated reporting capabilities are seeing significant improvements in their capabilities and the time it takes to perform the analysis. This innovative application of RAG serves as a bridge between the vast data lakes of unstructured data and the actionable insights that businesses need daily to drive key decisions and innovation. By utilizing RAG for automated reporting, companies can significantly streamline their reporting processes, enhance accuracy, and uncover valuable insights hidden within their data. Let’s start with how it can be utilized in this environment.READ FULL ARTICLEBuilt something cool? Tell us.Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled.📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️We would love to know what you thought—your feedback helps us keep leveling up.👉 Drop your rating hereThanks for reading,The AI_Distilled Team(Curated by humans. Powered by curiosity.)*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

By Stephane EyskensCloudPro #119WATCH NOWToday’s CloudPro Expert Article comes from Stéphane Eyskens, a Microsoft Azure MVP and seasoned solution architect with over a decade of experience designing enterprise-scale cloud systems. Stéphane is the author of The Azure Cloud Native Architecture Mapbook (2nd Edition), a comprehensive guide featuring over 40 detailed architecture maps that has earned 5.0 stars on Amazon and become an essential resource for cloud architects and platform engineers.In the article below, Stéphane tackles one of the most challenging aspects of Azure architecture: building truly resilient multi-region systems, with concrete examples using Azure SQL, Cosmos DB, and Azure Storage, complete with code samples and Terraform scripts you can adapt for your own DR testing.Happy reading!Shreyans SinghEditor-in-ChiefSAVE THE ARTICLE FOR LATERThe Azure Cloud Native Architecture Mapbook, Second EditionDesign and build Azure architectures for infrastructure, applications, data, AI, and security.Get 40% off eBookAnd 20% off PaperbackFor the next 72 HoursGET THE BOOKI like to say that Azure is simple… until you go multi-region. The transition from a well-designed single-region architecture to a truly resilient multi-region setup is where simplicity gives way to nuance. Concepts that seemed abstract (high availability versus disaster recovery, failover semantics, DNS behavior, data replication guarantees) suddenly become very real, very concrete, and sometimes painfully operational.This article is written for architects and senior platform engineers, who already understand the fundamentals but are required to build solutions that must remain available despite regional outages, service failures, or infrastructure-level incidents. The scope is intentionally narrowed to Recovery Time Objective (RTO). Data corruption, ransomware, and backup-based recovery are explicitly out of scope. Instead, the focus is on how applications and data services behave during live failover scenarios, and how architectural decisions, sometimes subtle ones, can make the difference between a seamless transition and a prolonged outage.Through concrete examples using Azure SQL, Cosmos DB, and Azure Storage, this article explores how replication models, DNS design, private endpoints, and SDK behavior interact at runtime, and what architects must do to ensure their applications remain functional when regions fail.Rather than focusing on theoretical patterns, the goal here is pragmatic—minimizing downtime and operational friction when things do go wrong. You’ll see diagrams, Terraform and deployment scripts, plus .NET code samples you can adapt for your own DR tests and game days.Before getting into the details, let’s briefly revisit the difference between high availability (HA) and disaster recovery (DR).HA and DR exist on a spectrum, with increasing levels of resilience depending on the type of failure you want to withstand:Application-level failures: In some cases, you may simply want to tolerate application bugs—for example, a memory leak introduced by developers. Running multiple instances of the application on separate virtual machines, even on the same physical host, can already prevent a full outage when one instance exhausts its allocated memory. That is for instance, what you would get if you spin up 2 instances of an Azure App Service within the same zone (no zone redundancy).Hardware failures: To handle hardware failures, workloads should be distributed across multiple racks. That is what you would get if you’d host virtual machines on availability sets.Data centre–level outages: To withstand more severe incidents, workloads should be spread across multiple data centers, such as by deploying them across multiple availability zones. You can achieve this by turning on zone-redundancy on Azure App Service or use zone-redundant node pools in AKS. With such a setup, you should survive a local disaster such as fire, flooding, etc.Regional outages: Finally, to survive major outages, such as a major earthquake, a country-level power supply issue, etc., workloads must be deployed across geographically distant data centers. You can achieve this by deploying workloads across multiple Azure regions in active/active or active/passive mode.Looking at Azure SQLLet’s first analyse the different data replication possibilities with Azure SQL. Table 1 summarizes the different capabilities.Table 1 – Replication capabilitiesWe’ll set aside named replicas and geo-restore, as the former does not contribute to disaster recovery and the latter is likely to introduce significant downtime and potential data loss. This leaves geo-replication as the remaining option. As you might have understood by now, using Azure SQL’s built-in capabilities, you cannot achieve a full ACTIVE/ACTIVE setup since it doesn’t support multi-region writes. This means that you can only have one read-write region and the secondary region(s) are read only.Table 2 outlines the two available geo-replication techniques.Table 2 – Geo replication optionsActive geo-replication may require updates to connection strings or DNS records to point to the new primary after a failover. That said, the actual impact depends on where (*) the client application is located as well as how you deploy to both regions. Let’s look at this in more detail. Figure 1 illustrates an active geo-replication setup between Belgium Central and France Central.Figure 1 – SQL geo replication with active geo replicationIn such a setup, under normal circumstances:Workloads in the primary region (Belgium Central) can connect to the primary server in read/write modeWorkloads in the primary region can perform read-only activities against the secondary replica, providing they tolerate the extra latency incurred by the roundtrip to the remote region (France Central).Workloads in the secondary region (if any), can perform read-only operations against the read replica with no extra latency.The configuration shown in Figure 1 supports a database-only failover. Both regions expose private endpoints to both SQL servers and rely on region-scoped DNS zones.Although Private DNS zones are global by design, keeping them regional allows each region to resolve both the primary and secondary servers. This requires four DNS records in total—primary and secondary endpoints registered in each regional zone.With a single shared DNS zone, this would not be possible: while all four private endpoints could be deployed, only two DNS records would be registered, since the endpoints map to just two FQDNs (primary and secondary). While this approach works, it keeps the regions siloed and prevents any cross-region traffic. From a resilience standpoint, it is preferable to provide as many fallback paths as possible.Moreover, as we will see later, with other resources such as Storage Accounts, a single DNS zone would force us to update the DNS records upon failover, causing a minimal downtime. Bottom line: using multiple DNS zones prevents issues during failover.Back to active geo replication! In case of failover, SQL servers switch roles: the primary becomes secondary and vice versa. This concretely means that the connection string primary.database.windows.net targets the read/write region in a normal situation but a read-only or unavailable one after failover. Workloads using this connection string would either stop working (if the regional outage persist), either talk to a read-only database instead of a read-write one, once the failover completed. Similarly, the connection string secondary.database.windows.net usually targeting the read-only region under normal circumstances now targets the read-write one after failover.Knowing this, a few options exist:You may choose to fail over everything (database+compute). In that scenario, workloads running in the secondary region can use their default secondary connection string, which will automatically target the new primary after failover. This approach requires the deployment pipeline to be region-aware, detect the target region, and apply the appropriate connection string. When deployed in the primary region, the application should use primary.database.windows.net, while in the secondary region it should already be configured with secondary.database.windows.net. This design eliminates the need for any connection string changes after failover. If your webapps, K8s pods, etc. are already up and running, the only thing you still have to do is route traffic to them. Any other SQL client not running in the secondary region (eg: on-premises), would have to update its connection string to target the new primary.You may choose to redeploy the compute infrastructure (web apps, etc.) to the secondary region only in case of regional outage. This approach is cheaper but risky as you’re not guaranteed to have the available capacity and it is causing a significant downtime. However, such an approach allows you to adjust your pipelines, specify the right connection string and simply redeploy your infrastructure and/or application package.If you want to deploy the application with the exact same settings in both regions, you’ll need to update the connection string used by workloads in the secondary region, since primary.database.windows.net will now resolve to an unavailable server after failover. If the original primary later comes back online, it will return as a secondary (read-only) replica, which would not support write operations. You can as well make your application failover aware (**).You can’t simply update DNS, meaning making secondary target primary and vice versa, because the FQDN (primary-or-secondary.database.windows.net) is validated by the target server, and the names must match—so redirecting it to a different server would simply fail.In conclusion, when using active geo-replication as the replication technique, you should make your applications failover-aware (**) and pre-provision both connection strings and implement the failover/retry logic in the application code itself. You may wrap your Entity Framework context into a factory to abstract away the retry logic. Given we typically use a scoped lifetime, you may expect some HTTP requests to fail (in case of an API) but new instances targeting the right server would ultimately succeed without having to restart the application. You may as well use a geo-redundant Azure App Configuration and failover it along SQL, then switch the primary server connection string after failover. The SDK allows you to monitor a sentinel key and to reload the configuration without having to restart the application:Read The Full Article by Stephane Here18 cloud architecture books in one bundle including AWS for Solutions Architects, Kubernetes for Generative AI Solutions, and more.2000+ Bundles already sold.Get The Bundle at$858$5.9048-Hour Flash Sale: 40% off with code FLASH40Book Your Seat NowEarly Bird Offer LIVE Now: 40% Off With Code EARLY40Book Your Seat NowWebinar: How to Build Faster with AI AgentsSave Your Seat📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}