devopsy.kinsta.cloud

Author: Mike Vizard

System Initiative Adds AI Agents to Infrastructure Automation Platform

System Initiative today announced it has added autonomous artificial intelligence (AI) agents to its infrastructure automation platform

Company CEO Adam Jacob said AI agents will now be able to interact with digital twins of IT infrastructure environments that System Initiative creates to enable DevOps teams to propose and safely execute changes to IT infrastructure.

DevOps engineers using natural language prompts can simply tell the System Initiative platform what outcome they want and the AI agent determines how best to accomplish that task. DevOps engineers then review that plan, which, if approved, will be executed by the AI agent, said Jacob.

AI agents will be able to complete tasks in a matter of minutes that previously would have required weeks for DevOps teams to manually perform, he added. AI agents will also be able to propose validated changes to IT infrastructure environments to optimize workflows and eliminate bottlenecks, said Jacob.

Additionally, DevOps teams can import existing infrastructure into the System Initiative platform to create a real‑time digital twin of every element of their IT environment that they can then use AI agents to help them explore and uncover hidden relationships between resources, noted Jacob. The digital twin platform created by System Initiative essentially functions as a knowledge graph that AI agents can use to understand the relationships between resources, he added.

DevOps teams will also be able to define custom guardrails and rules to limit the scope of the functions any AI agent is allowed to perform, noted Jacob. Built‑in policy checks and tests ensure every change adheres to compliance and operational requirements, he said.

In effect, AI agents will be integrated into existing DevOps teams without requiring any changes to existing workflows, added Jacob.

Ultimately, the goal is to make it much simpler for DevOps teams to employ AI agents to consistently employ best infrastructure management practices in a way that, for example, eliminates misconfigurations that might be exploited by cybercriminals, said Jacob.

As more menial infrastructure management tasks are automated, DevOps teams should also have more time to research how to optimize their environments for complex applications, he added.

It’s not clear to what degree DevOps teams are ready to rely on AI agents to manage IT infrastructure, but it’s apparent that, given the number of misconfigurations that exist, existing workflows are deeply flawed. It’s simply too easy for developers using tools such as Terraform to make a mistake. Of course, AI agents might also be prone to making mistakes, but hopefully, there will be fewer of them, especially as those AI agents become more familiar with the specific IT environment they have been tasked to automate.

No matter how IT infrastructure is provisioned, there will always be a requirement for it to be reviewed by a DevOps engineer. The challenge and the opportunity now is to automate the management of IT infrastructure at a level of scale that might not have not too long ago seem unimaginable.

August 27, 2025
Qwiet AI Extends Microsoft Support in Platform for Fixing Vulnerabilities

Qwiet AI today extended the reach of its application security platform that uses artificial intelligence (AI) agents to discover and remediate vulnerabilities in code to now provide deeper integrations with Azure DevOps, Azure Boards and GitHub platforms from Microsoft.

The company has also enhanced its support for webhook notifications, automated data export processes, expanded secrets configuration and made minor user interface (UI) enhancements.

Additionally, Quiet AI is also beta testing observability graphs and reports to provide deeper vulnerability trend analysis.

Finally, the AutoFix AI agents developed by Qwiet AI to remediate vulnerabilities have also been extended to add an ability to analyze data stored in a Static Analysis Results Interchange Format (SARIF) for non-Code Property Graph projects and policy support for code built using the Swift programming language used to build macOS applications.

Quiet AI CEO Stuart McClure said integration with the DevOps platforms from Microsoft comes at a time when they are gaining increased traction. Many application developers are increasingly relying on artificial intelligence (AI) coding tools from Microsoft to build software, he added.

The challenge is that much of that code can contain vulnerabilities because the AI models that were relied on by AI coding tools were trained using examples of flawed code. Unfortunately, too many developers put too much faith in the output of AI coding tools. In some cases, the code created by an AI coding tool might be more secure than the code an inexperienced developer might create, but there is no way to tell without scanning it. The only way to address that issue at scale is to make use of an application security platform such as Qwiet AI that has trained AI agents to both discover vulnerabilities and automatically remediate them, said McClure.

It’s not likely that concerns about vulnerabilities might be holding back adoption of AI coding tools so it’s probable the number of them that will find their way into production environments is only going to increase. The best way to proactively address that issue is to use AI to identify and remediate vulnerabilities as code is being written, added McClure.

In the case of Qwiet AI, that means using a platform that makes use of AI agents to run both static assessment security test (SAST) and software composition analysis to discover vulnerabilities that are then remediated using a series of AI agents that create, test and review changes that will be made to the code. In effect, Qwiet AI is making a case for using AI agents to compensate for the security flaws in AI coding tools.

Hopefully, the code being generated by AI coding tools will steadily improve as they are trained using examples of code that have been vetted for security flaws. In the meantime, however, DevSecOps teams should view any code generated by AI coding tools with at least the same level of suspicion as they would code written by human developers.

August 27, 2025
Broadcom Adds Argo and Ubuntu Support to VMware Cloud Foundation

Broadcom today at the VMware Explore 2025 conference unfurled a set of developer services for the instance of Kubernetes it has embedded into its VMware Cloud Foundation (VCF) platform that includes the Argo continuous delivery (CD) platform.

At the same time, Broadcom expanded its alliance with Canonical to include enterprise-grade support for instances of VCF running on top of the Ubuntu OS platform.

The instance of Kubernetes that is embedded into VCF will also now support Chiseled Ubuntu containers, which minimize footprints by removing unnecessary packages, utilities, and libraries to enable faster download times, reduced storage costs, and faster startup.

Finally, Broadcom is working with Canonical to enable faster deployments for artificial intelligence (AI) workloads using precompiled drivers for graphical processor units (GPUs) to both consume infrastructure more efficiently and reduce dependencies on external repositories to download drivers.

Broadcom, in the wake of acquiring VMware, has shifted its platform strategy to place more emphasis on running containerized applications on an instance of Kubernetes that is embedded in VCF. The VMware Tanzu platform, in contrast, is now based on an instance of the Cloud Foundry platform-as-a-service (PaaS) environment rather than an instance of Kubernetes.

In furtherance of that strategy, Broadcom is now adding a set of developer services to vSphere Kubernetes Service (VKS) that, in addition to Argo, add support for the Istio service mesh, Postgres and MySQL database and, via a VCF Advanced Cyber Compliance service add-on, support for hardened containers.

Krish Prasad, senior vice president and general manager for the VCF division of Broadcom, said developer services will make it possible for organizations that have adopted VCF to more easily support application developers without needing to set up separate teams for managing virtual machine and container environments. As Kubernetes and containers become more widely deployed, there is a clear need for operational efficiencies that also address compliance and security concerns, he added.

IT teams will be able to use best GitOps practices using Argo CD, while still being able to apply the controls that IT teams require to centrally manage IT infrastructure. Broadcom is also previewing access to a technical preview of an artificial intelligence (AI) agent it has trained to automate IT operations tasks and workflows.

In general, Broadcom is clearly making an effort to unify DevOps workflows and IT infrastructure management that within many enterprise IT organizations are typically managed by administrators rather than software engineers. That effort aligns with the rise of platform engineering as a methodology for managing application environments at scale that Broadcom is positioning VCF to address.

It’s not clear to what degree enterprise IT organizations are standardizing on VCF but for its second fiscal quarter Broadcom reported $6.6 billion in infrastructure software revenues, up 25% year over year. Third-quarter fiscal 2025 infrastructure software revenues projected at $6.7 billion, representing a 16% year-over-year increase. Broadcom also announced today that Walmart has agreed to standardize the management of virtual infrastructure on VCF, including providing its developers with access to Kubernetes clusters via VKS.

Daniel Newman, CEO of the Futurum Group, said those results show that despite recent controversies involving changes to the VMware licensing program, most customers are continuing to rely on VMware to manage their IT environments. While there may have been some defections, even many smaller organizations are opting to stay with VMware, he added.

It may be a while before IT organizations unify the management of IT environments. Many DevOps teams were created to provide a more agile approach to building and deploying applications that didn’t rely on a highly centralized IT team. However, many of those same DevOps teams are now spending more time managing IT infrastructure than they are writing code. As such, there is now a clear need to strike a balance between DevOps freedom and the need to optimally manage IT infrastructure.

August 26, 2025
Harness Delivers on AI Promise for DevOps

Harness today made a DevOps platform infused with artificial intelligence (AI) capabilities that enable agents to automate a wide range of tasks and workflows generally available.

Trevor Stuart, senior vice president for Harness, said the Harness AI platform is based on a knowledge graph that has been created to enable DevOps teams to manage, govern and observe tasks assigned to AI agents. The overall goal is to provide a framework for incorporating AI agents into DevOps workflows that will eliminate much of the manual effort that is often encountered when setting up pipelines, he added.

At the core of the Harness AI platform is a Software Delivery Knowledge Graph that continuously updates data pulled from both Harness and third-party DevOps tools and platforms across every stage of the software development lifecycle (SDLC). That knowledge graph enables AI agents to generate pipelines, rollback deployments and automatically run root-cause analysis.

For example, via a natural language, it is now possible for DevOps teams to simply describe their intent for setting up a pipeline, which Harness AI will build and deploy in adherence to the policies and guidelines an organization has defined, said Stuart.

Harness AI can also create, update, and maintain tests and chaos experiments, detect vulnerabilities and surface actionable insights to help reduce cloud costs, he added. None of the data collected by Harness AI, however, will be used to train any of the underlying AI models that Harness agents are invoking.

Harness claims existing Harness customers who participated in the Harness AI beta program have already seen downtime being cut in half, along with a 50% reduction in the amount of time spent debugging pipelines. Additionally, Harness reports test cycle times reduced by 80% and test maintenance efforts cut by 70%.

While DevOps teams are clearly embracing AI coding tools, the software engineering workflows used to deploy software don’t easily scale because of a dependency on overly brittle scripts. Harness is making a case for an AI platform that assigns tasks to AI agents that have been specifically trained to perform them. That approach makes it possible to not only build and deploy applications at greater scale, it also reduces the number of pipelines that need to be maintained by better optimizing workflows, said Stuart.

At this juncture, it’s not a question of whether DevOps teams will be relying on AI agents to automate tasks so much as it is when and to what degree. The one certain thing is that DevOps engineers will be needed to not only review the output created by those AI agents, but also orchestrate the management of workflows that might soon include hundreds of AI agents.

Hopefully, thanks to the rise of AI agents, much of the tedious manual effort that often conspires to burn out DevOps engineers will soon be reduced, if not entirely eliminated. The challenge and the opportunity now is building, deploying and maintaining the platform used to orchestrate the management of a portfolio of AI agents that, much like humans, will all seem to have their own set of unique personality attributes.

August 26, 2025
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

The Job Network

Bank of America
New York City
Vice President – Feature Lead
$189,600 to $199,600

Dice.com

General Motors
Remote
Staff DevOps Software Engineer – GM Commercial Software
$177,000 to $270,900

Indeed.com

SHINEHIRE
New York City
DevOps Engineer
$175,000 to $250,000

SimplyHired.com

Haymarket Media
New York City
DevOps Engineer
$150,000 to $165,000

LinkedIn

BAE Systems
Annapolis Junction, MD
DevOps Software Engineer
$146,700 to $249,300

August 25, 2025
SRE.ai Looks to Unify DevOps Workflows Across Multiple SaaS Applications

SRE.ai this week revealed plans to build a DevOps platform for deploying custom software across multiple software-as-a-service (SaaS) application platforms.

Fresh off raising $7.2 million in funding, SRE.ai CEO Raj Kadiyala said as the volume of code developed using low-code/no-code and, more recently, artificial intelligence (AI) tools increases, there needs to be a more automated approach to deploying that software based on best DevOps processes that also enables software development teams to monitor and observe those applications.

At the core of that effort is a platform that will leverage artificial intelligence (AI) technologies to automate the deployment of these applications that are already being developed at a pace that will overwhelm many existing DevOps workflows.

At the same time, organizations are also looking to centralize the management of custom extensions to SaaS application platforms versus having to build and maintain separate niche DevOps platforms for each SaaS application environment. Much of the software development effort that occurs across those platforms is similar enough to automate using a single platform as part of an effort to reduce the overall amount of tooling sprawl, said Kadiyala.

In effect, SRE.ai acts as a translation layer between business-critical platforms, developer workflows, and IT governance to automate DevOps workflows at scale in a way that is easily accessible to any developer regardless of their software engineering expertise, he added.

It’s still early days so far as usage of AI to automate the backend of software development workflows is concerned, but as more applications are built using AI agents and vibecoding tools, it may only be a matter of time before the teams currently tasked with deploying custom software on SaaS application platforms are overwhelmed.

In fact, many of the individuals currently deploying that software are administrators with little software engineering experience. As a result, many of those applications are not thoroughly tested for usability and scalability before they are deployed. More challenging still, much of that software is likely to contain known vulnerabilities that cybercriminals are likely to discover and easily exploit.

It’s not clear how much custom software is now being deployed on SaaS applications versus applications that are built from the ground up using best DevOps practices, but increasingly professional developers and software engineers are being asked to fix applications that were originally built by so-called citizen developers. Hopefully, as DevOps workflows continue to evolve those teams will work more collaboratively together as applications are built versus continually looking to professional developers to fix issues after an application has been deployed.

The next major challenge, of course, will be getting teams currently building custom SaaS applications to give up bespoke tools in favor of a more integrated platform. However, the one thing that everyone involved in the building of those applications can agree on is that nobody wants to be asked to fix an application in the wee hours of the morning when most of these issues seem to inevitably arise, in the absence of any AI tools to help them, they are not likely to understand how it was actually constructed in the first place.

August 22, 2025
MCP Emerges as a Catalyst for Modern DevOps Processes

Loreli Cadapan, VP of product at CloudBees, discusses how the Model Context Protocol (MCP) developed by Anthropic will provide the foundation for reinventing DevOps workflows.

Cadapan explains that MCP acts as a bridge between large language models and enterprise DevOps environments, functioning as a kind of control plane. It can connect with different CI/CD tools and testing frameworks, pull in real-time data on pipelines and vulnerabilities, and even orchestrate tasks across the software delivery lifecycle. By reducing context switching and integrating with existing developer tools, MCP is designed to improve both productivity and governance.

She compared MCP servers to factory floor managers—directing specialized AI agents that handle specific tasks such as diagnosing build failures or recommending tests. The challenge, she noted, is ensuring those agents are trained on the right data. As with any AI, the quality of output depends heavily on the quality of the inputs and feedback they receive.

Cadapan acknowledged that AI adoption varies across organizations. Some want co-pilot style assistance with humans in the loop, while others are experimenting with allowing agents to fully automate parts of their workflows. Either way, she stressed that guardrails and policies must be in place so that outcomes are reliable, secure, and aligned with enterprise requirements.

Looking ahead, Cadapan sees AI changing not just coding and pipelines but also product management practices, from drafting requirements to validating solutions. As AI-generated code and commits increase, bottlenecks are shifting downstream—from developers to reviewers and pipelines. Her advice for teams is to stay informed, experiment carefully, and validate outputs rather than trusting AI blindly.

The broader message: MCP and AI agents are early steps toward rethinking the entire software delivery lifecycle. As software delivery accelerates and AI-generated code becomes commonplace, the real challenge will be ensuring pipelines, governance, and teams evolve at the same pace. For DevOps practitioners, that means preparing now for a future where AI is embedded in every stage of the lifecycle.

August 21, 2025
How AI-Created Code Will Strain DevOps Workflows

Nick Durkin, Field CTO at Harness, explains why as artificial intelligence (AI) tools are relied on more to create code, it’s now only a matter of time before bottlenecks in existing DevOps workflows become exacerbated to the point where they need to be re-engineered.

Durkin noted that while AI is generating more code, organizations are not necessarily moving faster. In fact, industry reports show a slight slowdown, along with an uptick in bugs. The challenge, he said, lies in making it easier for engineers to do the right thing while putting guardrails in place to prevent errors, regardless of whether the code was written by a developer or an AI.

One issue is the proliferation of pipelines. In the past, teams often created a separate pipeline for every application, leading to thousands of variations that were difficult to manage. Durkin argued for a template-driven approach, where reusable pipelines with built-in policies make failures visible early and allow fixes to be made quickly — more like a video game that encourages iteration than a static approval process.

The rise of AI, he added, puts pressure on organizations to modernize their CI/CD platforms. Instead of stitching together dozens of tools, companies need scalable pipelines that can be automated and reused, with AI assisting in tasks such as test generation, troubleshooting and infrastructure management. The goal is to remove repetitive work so engineers can focus on higher-value problems.

Durkin also addressed the paradox that while AI can speed up some tasks, it may still extend delivery timelines. He sees this as part of a broader shift toward platform engineering, where context and governance matter more than raw speed. Increasingly, these conversations are moving beyond engineering leadership to CEOs, COOs and boards, as software delivery becomes central to business competitiveness.

Ultimately, Durkin emphasized that AI won’t replace the fundamentals of software delivery but will change how organizations approach them. Success will depend less on how much code is produced and more on how effectively pipelines, policies and platforms channel that output into secure, reliable applications. In his view, the companies that thrive will be those that treat AI as an accelerator for disciplined practices rather than a shortcut around them.

August 21, 2025
HoundDog.ai Code Scanner Shifts Data Privacy Responsibility Left

HoundDog.ai today made generally available a namesake static code scanner that enables security and privacy teams to enforce guardrails on sensitive data embedded in large language model (LLM) prompts or exposed artificial intelligence (AI) data sinks, such as logs and temporary files, before any code is pushed to production.

Company CEO Amjad Afanah said the HoundDog.ai scanner enables DevSecOps teams to embrace a privacy-by-design approach to building applications. The overall goal is to enable organizations to shift more responsibility for privacy left toward application development teams as code is being written, he added.

Since its initial availability last year, HoundDog.ai has already been used to scan more than 20,000 code repositories, from the first line of code using IDE extensions for VS Code, JetBrains and Eclipse to pre-merge checks in continuous integration (CI) pipelines. The approach has saved early adopters of HoundDog.ai thousands of engineering hours per month by eliminating reactive and time-consuming data loss prevention (DLP) remediation workflows.

DevSecOps teams can also use HoundDog.ai to block unapproved data types, including automatically blocking unsafe changes in pull requests to maintain compliance with data processing agreements.

Finally, HoundDog.ai will also generate audit-ready reports that map where sensitive data is collected, processed and shared, including through AI models, for Records of Processing Activities (RoPA) and Privacy Impact Assessments (PIAs) that are pre-populated with detected data flows and privacy risks that can be tailored for specific regulatory frameworks, such as the General Data Protection (GDPR) or Healthcare Information Portability and Accountability Act (HIPAA).

HoundDog.ai is designed to automatically detect all AI usage, including shadow AI tools, across both direct integrations to, for example, OpenAI and Anthropic as well as indirect ones to frameworks like LangChain, SDKs, and libraries, said Afanah. DevSecOps teams can now trace more than 150 sensitive data types, including personal identifiable information (PII), public health information (PHI), card holder data (CHD) and authentication tokens, back to risky sinks such as LLM prompts, prompt logs and temporary files.

That ability to trace sensitive data flows across layers of transformation and file boundaries is going to be especially critical as application developers rely more on AI tools to build code, which may result in sensitive data being inadvertently included in an application, said Afanah.

For example, with the explosion of AI integrations in application development, organizations are discovering sensitive data passed through LLM prompts, software development kits (SDKs) and open source frameworks without visibility or enforcement. Traditional AI security tools typically operate at runtime, often missing embedded AI integrations, shadow usage and sensitive data that is specific to a particular type of organization or industry. Without code-level visibility, understanding how that data entered an AI model or prompt is all but impossible, said Afanah.

It’s not clear how the rise of AI might require organizations to revisit the management of compliance with various privacy regulations, but as regulations eventually become more stringent it is now arguably only a matter of time before privacy, much like security before it, is shifted further left.

August 21, 2025
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

Dice.com

AMD
San Jose, CA
Senior Software Manager – DevOps
$184,590

LinkedIn

Salesforce
San Francisco
Senior Lead DevOps Engineer
$172,000 to $276,100

The Job Network

Frontera Health
Denver
DevOps Engineer
$125,000 to $145,000

Indeed.com

CareCentrix
Remote
Senior AWS DevOps Engineer
$125,000 to $140,000

SimplyHired.com

Morgan Stanley
Baltimore
Cybersecurity DevOps Engineer
$110,000 to $225,000

August 18, 2025