The Platform Behind the Next AI Breakthrough

This article has been written and paid for by Microsoft. The content is not WIRED editorial content. The content does not necessarily reflect the views of WIRED, its affiliates, or owner, and does not reflect any endorsement direct or indirect of Microsoft, its affiliates, or other clients.

The next frontier of AI is coming into focus as agents take on longer, more strategic work. They can write and validate code, coordinate across systems, rebalance inventory as conditions change, manage production operations, or carry a migration from plan to completion. AI is no longer supporting one step; it is executing and reshaping the process itself.

New models help drive this change by expanding what AI can reason about, create, and accomplish. But the combination of advances in models and infrastructure is key to moving agents from isolated tasks to sustained, high-impact work. Realizing that potential requires silicon, compute, storage, and networking to work together so workloads run faster and more reliably at scale.

Agents Are a Different Kind of Workload

Cloud infrastructure has evolved before to support new kinds of applications. Even the most advanced cloud-native applications often followed predictable request-and-response paths. To deliver this at scale, cloud-native architectures turned to containers, distributed services, and dynamic scaling, to make the platform more automated and flexible. Agentic applications build on that foundation but introduce another shift: long-running work whose path and resource needs are dynamic and often unpredictable.

An agent may work for hours or days, moving between reasoning, data retrieval, tool use, and code execution while preserving progress between steps. Its needs may shift throughout the task. It might require a burst of computing power for one step, fast access to large amounts of data for another, and then pause while another tool or another agent finishes its work. Supporting this ebb and flow requires the platform to evolve again—this time around how agents operate.

Infrastructure Must Work as One System

Every layer of infrastructure is evolving for AI. Compute must respond to the changing demand of agentic workloads. Storage must preserve progress across long-running work. And networking must move data quickly among models, tools, and business systems. But improving each layer independently is no longer enough.

Agent performance depends on how well those layers are evolving and how well they all work together. More computing power will not help if the platform cannot move data quickly or maintain progress between steps. Silicon, compute, storage, and networking need to be co-designed and continuously optimized as one system.

This integrated approach also changes infrastructure operations. Teams need a complete view of performance, capacity, and cost so they can find bottlenecks and improve the full workload instead of tuning one component at a time. That system-wide view helps turn infrastructure improvements into faster, more reliable, cost-effective workloads.

Different Workloads, One Operational Approach

That consistent operational view becomes even more important as teams choose different technologies for different workloads. A customer-facing agent may prioritize low latency, while a complex workflow may need deeper reasoning. Teams need the freedom to choose the right model, run time, silicon, and environment for each workload while balancing performance and cost.

But more choice can create operational complexity because each combination may require a different approach to deployment, monitoring, and optimization. An integrated platform preserves flexibility while giving teams a consistent way to operate across models and infrastructure. Teams can choose what works best without managing every combination differently.

Levi Strauss & Co. And the Value of an Integrated Platform

Levi Strauss & Co. began its path to AI transformation by preparing existing business systems for the future. The company moved its data centers to Microsoft Azure, consolidated ERP platforms, modernized applications, and unified its data in a governed, real-time layer. But that was only the beginning.

Levi Strauss & Co. is now building a unified “super agent” to coordinate more than 1,000 domain-specific agents. Its platform must support both the critical processes that run the business today and a new class of intelligent workloads that coordinates work across functions. The lesson is clear: A durable platform must be built for current applications and whatever AI makes possible next.

Three practical tests for an agentic platform

Technical leaders can begin with three questions to assess readiness for an agentic platform.

First, do I have the high-performance compute, storage, and networking that models and agents need—or am I trying to run tomorrow’s applications on yesterday’s platform?

Second, can I choose the right model, run time, silicon, and location for each agentic workload while balancing performance, cost, complexity, and risk?

Third, can I use agents to operate the platform by moving workloads into production faster, resolving incidents before disruption, and continuously improving performance, reliability, and AI economics?

Building for the next decade

The next decade of AI will not be defined by a single breakthrough. Models will become even more capable, agents will take on work we have not yet imagined, and infrastructure demands will shift with them. Organizations need a durable platform that can embrace each new wave of innovation without being rebuilt whenever the frontier moves.

Azure is the platform foundation for that future. Microsoft has spent years bringing some of the world’s most advanced and demanding AI solutions online at massive scale. The lessons from that work continually shape Azure silicon, compute, storage, networking, and operations—and make those capabilities available to customers.

This is the opportunity of Frontier Transformation: continually turning advances across the AI stack into new ways to operate, compete, and grow.

Learn more at azure.com.
The post The Platform Behind the Next AI Breakthrough appeared first on Microsoft Azure Blog.
Quelle: Azure

Haiku 5.5 now available on Kiro in AWS GovCloud (US)

A new Anthropic model is now available in the Kiro IDE and CLI for the AWS GovCloud (US) Regions. With Haiku 5.5, all three Claude 5.5 models are now available in Kiro in AWS GovCloud (US).
Claude Haiku 5.5 is the fastest model in the Claude 5.5 family. It’s a clear step up from Claude Haiku 4.5, built for work that runs in large volumes and needs quick responses. Developers can use it to summarize and compact long context, classify and route requests, process text in bulk, and make small, targeted edits across many files. It also works well as a review subagent that runs in parallel, while Opus 5.5 and Sonnet 5.5 take on harder planning and coding work. Haiku 5.5 uses adaptive thinking across five effort levels from low to max, with medium as the default. It’s available with a 1M context window and a 0.06x credit multiplier. Requests over 100K tokens are billed at a 0.3x multiplier.
Ensure your IDE or CLI is updated to the latest version, then restart it to access the new model from the model selector. For more details, visit the GovCloud documentation, the monitoring and tracking guide, or contact your AWS account team. To learn more about Kiro, visit the Kiro product page.
Quelle: aws.amazon.com

Amazon RDS for Oracle now supports M8i and R8i instances in additional AWS Regions

Amazon Relational Database Service (Amazon RDS) for Oracle now supports M8i and R8i instances in additional AWS Regions. Both M8i and R8i instances are now available in Asia Pacific (New Zealand) and Canada West (Calgary), and R8i instances are now available in Europe (Milan, Stockholm, and Zurich) and Israel (Tel Aviv).
These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The M8i and R8i instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances.
M8i and R8i instances are available for Amazon RDS for Oracle in Bring Your Own License model for Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2). For EE, instances are available in sizes from large to 48xlarge, as well as bare metal sizes metal-48xl and metal-96xl. For SE2, instances are available in sizes from large to 4xlarge and bare metal sizes metal-48xl and metal-96xl. Refer to Amazon RDS for Oracle Pricing for available instance configurations, pricing, and region availability.
Quelle: aws.amazon.com

Jeffery Lim wants to open up cloud research

Millions of people rely on the cloud every day, yet researchers working to secure and improve the cloud are often locked out of the technology it runs on. Jeffery Lim is trying to change that.One such technology, single-root input/output virtualization (SR-IOV), lets major cloud providers like Amazon Web Services boost the performance of their systems. Cloud providers combine SR-IOV with custom hardware and software to deliver faster cloud services than traditional setups allow.Lim is a computer engineer at MIT Lincoln Laboratory. While pursuing his master’s degree, he noticed that academic research proposing smarter or safer cloud infrastructure did not account for SR-IOV. Very few open-source hardware platforms existed that would let researchers work directly with SR-IOV as they developed new software or models to improve the cloud.So, Lim filled that gap with his master’s thesis, building a hardware platform compatible with SR-IOV that captures key features of the infrastructure used by cloud providers. The result is a research and development platform that allows researchers to build, test, and evaluate realistic cloud designs.”As a society, everyone uses the cloud now,” Lim says. “I hope that people will use this platform to build systems more akin to how cloud providers operate, and, more broadly, that researchers everywhere can take advantage of SR-IOV for the performance benefits it offers.”Lim presented a paper at the IEEE High-Performance Extreme Computing Conference in September, and he plans to release this work open source. In recognition of the hardware’s potential impact, the Boston University College of Engineering awarded Lim its 2026 Master’s Societal Impact Award.Winding path to cybersecurityGrowing up, Lim was fascinated with building computers. That fascination set him on a winding path to the Secure Resilient Systems and Technology Group at Lincoln Laboratory.After studying electrical and computer engineering at the University of Colorado at Boulder, he spent several years in industry working on field-programmable gate arrays (FPGAs) — integrated circuits that can be repeatedly reprogrammed for different functions. This background led to his 2018 hire at the laboratory, where he developed FPGAs for small radars that detect the breathing of humans trapped in rubble and for large radars that track satellites and orbital debris from atop Millstone Hill. He next turned to investigating radiation-induced faults in integrated circuits aboard space platforms and ways to protect the chips.This focus on safeguarding hardware turned his attention to cybersecurity, an area he had long found interest in but never fully explored. So, Lim transferred to his current group in 2022 to pursue this interest.”I don’t think there’s another place in the world where I could say, ‘Hey, I want to try something new,’ and actually have that opportunity,” Lim says. “Lincoln Laboratory does that really well, giving people the chance to transition into a different field of engineering or area of expertise.”To deepen his expertise, he applied and was accepted in 2024 to the Lincoln Scholars Program, which allows technical staff to pursue full-time graduate studies at the master’s or doctoral level. Scholars remain employed and apply their academic work to national security needs.As he pursued his master’s degree at Boston University, Lim worked closely with his Lincoln Laboratory mentor Benjamin Nahill, who helped Lim keep his research tied to the laboratory’s mission and provided a sounding board for ideas. “I’d call him my rubber ducky,” Lim says, a nod to the classic software engineering practice of “rubber duck debugging” — reasoning things out through speaking aloud to a rubber duck.”We talked about his thesis concept for quite a while, but the implementation suddenly became real toward the end,” says Nahill. “He transformed what began as an idea for a research tool to an incredibly performant implementation. Having worked with Jeffery on projects in our group, I shouldn’t have been surprised, but it was most impressive.”Missing piece for cloud researchWhen most people say “the cloud,” they mean cloud services provided by companies such as Amazon, Microsoft, and Google. Virtualization is the invisible process that makes those services possible. Users uploading photos or streaming movies are interacting with virtual machines, running on servers in data centers.In the past decade, SR-IOV has changed the way traditional virtualization works. Traditionally, a software component known as the hypervisor acts as a middleman between the virtual machines and the server’s physical hardware. Its main role is to distribute computing resources as needed, and provide functions (such as networking) to the virtual machines.In contrast, SR-IOV enables virtual machines to access a dedicated slice of the hardware device, reducing the hypervisor overhead. This capability is often found on specialized devices called Smart Network Interface Cards (SmartNICs). Major cloud platforms, such as Amazon Web Services’ Nitro System and Microsoft’s Azure Accelerated Networking, use SmartNICs with SR-IOV to provide high-speed networking to many virtual machines.”This was the missing research capability that I set out to build: an open-source SmartNIC platform compatible with SR-IOV,” Lim says. Because SmartNICs can be built on FPGAs, Lim had a familiar starting point. In fact, he found a couple of platforms that supported SR-IOV; however, they lacked features that would make them practical tools for researchers to utilize. He spent a couple of months building those required components.The first component is a switching fabric. Often, cloud customers’ applications or programs may be split across multiple virtual machines, which may be distributed across multiple servers. Therefore, the cloud system needs a way to determine whether a piece of data needs to stay local or leave the server. Lim’s switching fabric handles that traffic-directing job.The second component is a network function engine. This small, specialized processor is built to run security rules, like firewalls, that keep one customer’s data safely separated from another’s. To build it, Lim used Extended Berkeley Packet Filter, a system that allows custom code to inspect network traffic, approving or blocking data as needed. Rather than starting from scratch, he adapted the design from doctoral research conducted by Zaid Tahir, who studied under their shared advisor, Professor Martin Herbordt of Boston University.Herbordt described Lim’s efforts as “combining vision with heroic system-building, single-handedly putting together the hardware and software necessary to prove the concept.””I’m very much a hands-on hardware guy, so, yes, I ended up building two servers at home to test my designs,” Lim says. He also used the Mass Open Cloud’s Open Cloud Testbed, a test bed meant for testing cloud platforms, to gather metrics and validate his system.Impact beyond the platformThroughout the project, Lim maintained focus on applicability and transparency. “My biggest worry was, is this going to be relevant? Is this something that can actually be utilized?” Lim reflects.  Earning the Societal Impact Award points to yes. The award recognizes projects that address pressing global challenges through innovative applications of technology. Lim’s platform opens the door to evaluating a cloud trust model that remains largely opaque, even as many sectors of society, from health care to banking, increasingly rely on it.”Right now, we have limited visibility into how major cloud providers implement and evolve their infrastructure,” Lim says. “If researchers can catch up to how the modern cloud operates, they might develop new approaches that improve security or performance.”As part of his role as a Lincoln Scholar, Lim is also exploring how his work connects to Lincoln Laboratory’s national security mission. He sees several potential avenues.One program in his group is evaluating cyber-hardening techniques for hypervisors, as the cloud takes on an expanding role in defense systems. “Introducing Lim’s SR-IOV-compatible platform to this work would help bring our research closer to how cloud environments operate, demonstrating the impact of our techniques in realistic environments,” says Nathan Burow, the program’s principal investigator. Another program is examining what it takes to stand up high-performance data centers, work that could draw directly on Lim’s research into the technologies SR-IOV requires.Or his path may lead to something new. The laboratory allocates internal technical investment funding — supported by the Office of the Under Secretary of War for Research and Development — for staff-proposed projects addressing emerging needs. Lim is considering a proposal to bring SR-IOV to the embedded-systems level, say, to drones or satellites.”We saw how cloud providers moved from traditional virtualization to SR-IOV to maximize performance while maintaining isolation,” he says. “I wonder if we can do the same at the embedded level, where power and compute constraints are even tighter. I’d love to complete that transition. I learned a lot from the cloud. Now, can we apply those lessons somewhere else?”
Quelle: Massachusetts Institute of Technology

The Ecosystem is the Answer: Docker, the Blueprint Alliance, and the Importance of Collaboration in the Agentic Era

In 2025, the average global Fortune 500 enterprise had fewer than 15 AI agents in use. Gartner expects that number to explode to more than 150,000 by 2028. Only 13 percent of organizations think they have the right governance for today, and AI is evolving and moving faster than any other technology paradigm in our history. 

Today, most security leaders face an agentic Sophie’s choice: allow AI adoption without the right controls and hope for the best, or block it and become the obstacle to a priority the business has already set (while adoption continues in the shadows, anyway). 

But there is a third option, one in which organizations fully tap into the transformative power of agentic agents without having to live in fear of a catastrophic security failure. The good news is history has already shown how to get there.

How technology gets adopted

Historically, technology adoption has followed a reliable pattern. A capability spreads, each vendor and team builds its own approach, the inconsistency becomes expensive and ineffective, leading the industry to agree to a shared baseline. Adoption then accelerates.

Take the credit card industry, for example. The big credit brands each ran their own security programs, and merchants had to satisfy every one. This was, of course, cumbersome for the vendors, but also for the customers, too. Every time a customer wanted to adopt a new technology, they had to manually build out security and adoption plans, adding significant adoption and procurement delays. 

Rather than continue on their own, the credit card companies came together to create PCI DSS 1.0 in December 2004 and formed the PCI Security Standards Council in 2006. Buyers now had one thing to ask for, and auditors had one thing to check. Collaboration overcame complexity, and everyone benefited.

Security-minded technologists can find another example closer to home in the Center for Internet Security (CIS). In the early days of the internet, cybersecurity was a veritable wild west. Enter the CIS, which was founded in 2000 because the industry lacked prescriptive, recognized hardening standards. To this day, each CIS Benchmark is built by consensus among volunteer experts, vendors, and academics.

Docker itself is no stranger to the development and furthering of shared standards. As just one example, in 2015, Docker contributed its image format and runC runtime to the Open Container Initiative. A shared baseline at the runtime layer let registries, runtimes, and clouds interoperate.

Each of these examples played a different role: PCI mandated, CIS guided, and OCI standardized. 

Agents will need elements of all three.

Where enterprise AI is on that curve

Agents are in the everyone-builds-their-own phase. Each product solves a real piece of the problem, but which piece, and how the pieces fit, is rarely clear. “Agent governance” or even “AI governance” can mean almost anything.

Adoption is incremental. A developer allows common commands, widens filesystem access, then connects GitHub, a cloud tool and an MCP server, each through a prompt that makes sense on its own. The logs mostly record that the developer acted. Security is often the last to know.

The risks: familiar and new

Much of the risk is familiar. Shadow adoption looks a lot like early SaaS and cloud. Credential sprawl and supply chain exposure are old problems. Inventory, least privilege, audit, and joiner-mover-leaver discipline still apply.

Several things are new, however:

Runtime reprogramming. Users — and the agents themselves — can change what an agent does in plain language, with no release cycle.

Borrowed identity. An agent typically assumes the user’s identity, so anything the user can read, change, or delete, the agent can too.

Autonomy. Agents act asynchronously, spawn sub-agents, and pass authority down the chain. A bad instruction can become an action before anyone steps in.

Risky combinations. A tool with no known vulnerability can still complete a path from untrusted input to sensitive data to external communication.

A shorter response window. Containment has to revoke credentials and sessions, not only kill a process.

Existing controls still matter, but they were not built for this mix of programmability, access, and autonomy.

How the Blueprint Alliance and Agent Baseline respond

As with the credit industry and PCI, cybersecurity and the CIS, and containerization and OCI, collaboration offers the answer to this new era of complexity. That’s why Docker is not just focused on building tools like Docker Sandboxes and Docker AI Governance, but in taking an ecosystem-centric approach to problem solving for our customers. Docker is already a founding participant in multiple initiatives aimed at developing valuable reference architectures for AI security workflows, including the Blueprint Alliance and Agent Baseline.

Both start from the same premise: no single vendor or enforcement point can solve this.

The Blueprint Alliance sets out how the pieces fit together. Okta, AWS, CrowdStrike, Databricks, Docker, Google Cloud, Lovable, Proofpoint, Salesforce, ServiceNow, Wiz and Zscaler are founding members, with GE Appliances and World Central Kitchen as strategic advisors. The open, multi-vendor reference architecture answers four questions: Where are my agents? What can they do? What are they doing? How do I respond? Members have committed to interoperability testing over MCP, OCSF, SSF and CAEP, and to publishing reference integrations.

Agent Baseline sets out what each piece must do and how to verify it. It fills the gap between threat lists, which say what can go wrong, and the capabilities an enterprise needs in place. It defines six outcomes (Discover, Constrain, Authorize, Observe, Validate and Respond) and 35 open, versioned controls, each with a permanent identifier you can cite in an audit finding or RFP response. Docker, Snyk and Keycard drafted it, and the current release is v1.0-draft. Controls stay where they already live, in the identity provider, the sandbox and the SIEM.

Blueprint says how the pieces connect. Agent Baseline says what must be true and can be checked. Both advise starting with discovery before policy, and testing the kill switch before you need it.

For partners, the roles are defined. The Alliance whitepaper assigns integrators the operating model: assessing a customer’s current stack against the architecture, assigning ownership across the pillars, tiering risk, and running lifecycle and compliance over time. Technology partners get a published target to integrate against, and the reference integrations cross vendor lines by design. The partners who can design, integrate and operate across those lines are the ones customers keep.

Where Docker fits

Most Alliance members govern agents from the outside: who the agent is, what it can reach, and what it is observed doing. Docker governs the inside: the environment where the agent executes. As agents move from suggesting actions to taking them, trust has to extend beyond the model to the places where they run. Docker has helped define a shared baseline for the runtime before, and now we’re working with the Alliance and our partners to make that baseline concrete, open, and testable in the age of agents.

The execution environment is where policy gets enforced and evidence gets produced.

That boundary is where policy gets enforced and evidence gets produced. The Alliance whitepaper notes that without runtime support to pause an agent without collateral impact, API-driven containment “provides a false sense of security.” Agent Baseline’s CON-03 calls for isolated, confined execution, which is the job of Docker Sandboxes, while Docker MCP Gateway and Docker Hardened Images cover tool access and the supply chain.The Secure Software Factory reference architecture, drafted in part by Docker authors, applies Agent Baseline to agents that ship code.

What to do next

Baselines let adoption move faster, not slower. Security and platform leaders can assess against the baseline today. Agent Baseline controls are citable, and Okta offers an AI Blueprint Assessment Tool. Partners can start with the Docker Navigator Partner Program, which rewards AI governance work, and with the reference integrations the Alliance publishes. Docker’s VP of Partners Leon Jones explains the program here. Download the blueprint at blueprintalliance.ai and read the controls at agentbaseline.org.

Quelle: https://blog.docker.com/feed/