devopsy.kinsta.cloud

Author: Mike Vizard

AWS Application Modernization Service Employs AI Agents to Refactor Code

Amazon Web Services (AWS) has made generally available a service that makes use of artificial intelligence (AI) agents to migrate and modernize infrastructure, applications and code.

Sriram Devanathan, general manager for low-code/no-code AppBuilder service at AWS, said the AWS Transform service provides access to a natural language chat interface that makes it simpler to either refactor, for example, VMware, mainframe and .NET workloads or upgrade older instances of Java.

Initially previewed as an extension to Amazon Q Developer, AWS has a series of AI agents that have been specifically trained to both understand how code is constructed and invoke various tools, such as application assessment tools from CAST Services, to transform it. DevOps teams can use AWS Transform to specify objectives, share project context, assess business plans and cost savings, review and adapt transformation plans, identify dependencies, approve code infrastructure suggestions and execute tests.

For example, an AWS Transform agent for .NET can migrate a Windows-based application to Linux up to four times faster than traditional methods to reduce Microsoft cost by as much as 40%, noted Devanathan.

Similarly, an AWS Transform agent for VMware can automate the conversion of on-premises VMware network configurations to AWS equivalents up to 80 times faster than manual approaches.

An AWS Transform agent for mainframe can also decompose monolithic z/OS COBOL applications into components that can be run in the cloud in minutes. AWS Transform agents can, for example, invoke a Graph Neural Networks capability that makes it simpler to refactor applications written in COBOL, JCL (Job Control Language) and those relying on Customer Information Control System (CICS) transaction manager, BMS (Basic Mapping Support) screens, DB2 databases, and VSAM (Virtual Storage Access Method) data files in a set of Java-based microservices that can be deployed on an open source Postgres database.

In many cases, the documentation generated after that migration is more detailed than what was created when those legacy mainframe applications were initially built and deployed, said Devanathan.

Most organizations will still require specialists to migrate and modernize applications, but the total amount of time and effort required will be substantially reduced, said Devanathan. Most IT teams would be well-advised to revisit their modernization strategy by picking a relatively small project to better understand the level of refactoring that can now be achieved using AI agents, he added.

Longer term, AWS is looking to expand the range of modernization tools that its AI agents are trained to invoke, he added.

During uncertain economic times, the amount of pressure being applied to reduce the total cost of IT always rises. As such, interest in migrating legacy applications always tends to rise in an economic downturn. However, the amount of effort historically required to modernize applications has been substantial. In fact, many of these projects have never been completed simply because the cost of migrating applications proved to be much higher than initially anticipated.

Regardless of motivation, however, the total cost of continuing to run legacy applications can, in many cases, be even more prohibitive than the cost of a modernization initiative that, with the help of AI agents, may now prove to be a more viable option.

May 28, 2025
Futurum Group Survey Surfaces DevSecOps Progress on Multiple Fronts

A survey of 110 security leaders finds all are investing in software supply chain security, with application security posture management (ASPM) and DevSecOps automation and orchestration topping the priority list, followed closely by security composition analysis (SCA) tools, application programming interface (API) security and dynamic application security testing (DAST) tools.

In addition, 30% of respondents expect to be piloting a software bill of materials (SBOM) initiative in the next 24 months, the survey finds.

However, the source of the funding for these initiatives is becoming more of a shared responsibility, with only 21% of respondents reporting that security budgets are the sole source. In fact, half of the respondents (50%) noted application development teams now own responsibility for application security.

Overall, only 25% of respondents said there is limited collaboration with application development teams, resulting in occasional friction, compared to 59% that said there is good collaboration with room for improvement. Only 16% said there is a tight partnership based on shared goals.

Fernando Montenegro, vice president and practice lead for cybersecurity, said it’s clear there is now more collaboration between application development and cybersecurity teams, with more security tools being incorporated into DevOps workflows. However, improving software supply chain security it’s also still very much a work in progress, he added.

The degree to which application developers might share the same level of optimism about the state of DevSecOps is less clear. Historically, many developers have resented requests to search for vulnerabilities in code that either never made it into an application running in a production environment or are only found in an application that isn’t accessible externally.

Additionally, no one is quite certain to what degree artificial intelligence (AI) is improving the quality of the code being generated versus making it worse. On the one hand, far too many application developers lack cybersecurity expertise, so much of the code they have generated has been flawed. However, general-purpose AI models have been trained using examples of flawed code randomly sourced from across the internet. Not surprisingly, large language models (LLMs) are generating code with known vulnerabilities.

Hopefully, the next generation of LLMs will be trained using examples of code that have been vetted for security flaws, which, when coupled with more advanced tools that are leveraging AI to discover vulnerabilities as code is being written, should eventually improve the overall state of application security.

Of course, it’s also safe to assume cybercriminal syndicates and various nation states are now also using AI to identify vulnerabilities in code, but also generate the code needed to exploit those vulnerabilities. As a result, the number of cybersecurity incidents involving legacy code is likely to dramatically increase in the months ahead.

DevOps teams at the very least should by now be reviewing what changes to existing DevOps workflows need to be made to achieve that goal. After all, the easiest vulnerability to fix is the one that was never created. The challenge, of course, is determining where the funding needed to acquire the tools and platforms that make it possible to build and deploy more secure applications is actually going to come from.

May 27, 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.

SimplyHired.com

SAP
Reston, VA
Expert DevOps Engineer
$220,200 to $374,200

Indeed.com

Google
Boulder, CO
Cloud Engineer – Platform and Infrastructure – Professional Services
$177,000 to $263,000

Dice.com

KLA Instruments
Milpitas, CA
DevOps/Supply Chain Management (SCM) Engineer
$130,400 to $221,700

LinkedIn

AVALON
Remote
DevOps Engineer
$110,000 to $140,000

The Job Network

HBS Systems
Richardson, TX
DevOps Architect
$105,000 to $115,000

May 26, 2025
Perforce Commits to Building Agentic AI Fabric for DevOps Workflows

Perforce Software this week committed to building an agentic artificial intelligence (AI) framework that will be embedded across its entire portfolio of DevOps tools and platforms.

Company CTO Anjali Arora said Perforce Intelligence will provide access to a set of AI agents that have been trained to automate a range of software engineering tasks that DevOps engineers will be able to orchestrate.

Additionally, Perforce is committing to using the same fabric to integrate its AI agents with other AI agents being developed by, for example, cloud service providers, she added.

Perforce, for example, has already added several AI agents to platforms such as Gliffy, a diagramming platform for Confluence, a Wiki-based collaboration platform for sharing documents provided by Atlassian.

Next month, AI agents will also be added to the Puppet Enterprise Advanced platform to provide access to Puppet Infra Assistant, a natural language chat interface that allows DevOps teams to quickly understand what is happening across the IT environments that have adopted the IT automation that Perforce acquired in 2022.

Via that interface, it becomes possible to know how something is working, what versions of modules or server operating systems are being used and whether something is in or out of compliance without needing to know the Puppet programming language.

Ultimately, the AI agents being developed by Perforce will make it simpler for DevOps teams to create and maintain workflows across a portfolio of DevOps tools and platforms that Perforce has acquired over the past few years, including BlazeMeter, an application testing platform, Delphix, a provider of data masking platform, and, most recently, Snowtrack, a provider of version control software used by application designers.

The overall goal is to eliminate the need for scripts, frameworks, or other legacy approaches to integration in favor of AI agents that share access to an orchestration framework. Those agents will be able to proactively adapt as changes are made across the entire software development lifecycle (SDLC), said Arora.

DevOps teams that have had early access to these AI agents have seen 50% efficiency gains while increasing test coverage by 20%, according to Perforce. In time, it’s apparent DevOps teams will be able to more effectively manage a much broader portfolio of applications, noted Arora. The challenge will be adapting existing DevOps cultures to include AI agents that software engineers are assigning tasks to complete, with the help of other AI agents and policy controls to provide governance functions and validations of output, she added.

It’s not clear to what degree DevOps teams have adopted AI, but research from the Futurum Group finds 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code. A total of 39% also plan to make use of AI models based on machine learning algorithms.

Regardless of what type of AI technologies are adopted, hopefully, DevOps workflows and pipelines will soon be reconstructed in a way that is significantly less fragile as more scripts and connectors are eventually replaced by AI agents.

May 22, 2025
Datadog Expands Scope of Observability Ambitions

Datadog is expanding the scope of its DevOps portfolio following a pair of acquisitions that add feature flagging and data observability capabilities to its portfolio of services.

At the same time, the company also launched a pair of open source projects through which Datadog is providing access to an open-weights AI model, dubbed Toto, specifically trained using time-series data in a way that makes it possible to instantly detect anomalies and capacity planning issues and BOOM, a time-series benchmark that provides access to 350 million observations across 2,807 real-world multivariate methods to capture the scale, sparsity, spikes and cold-start issues that are unique to telemetry data collected from production environments. .

Michael Whetten, vice president of product at Datadog, said that, in general, the provider of a widely used monitoring platform is now moving to expand the reach and scope of its offerings.

For example, the company is adding feature flagging and experimentation to its Product Analysis suite of tools following the acquisition of Eppo. That capability will make it possible to track code changes using feature flags that enable DevOps teams to create branches within applications, which makes it simpler to test or limit access to new capabilities in applications.

Additionally, data science teams and product managers will be able to design and measure impact with experiments, while business analysts can feature flags to understand overall usage patterns.

That move comes on the heels of a separate deal that involved the acquisition of Metaplane, a provider of a data observability platform that makes use of machine learning algorithms to prevent, detect and resolve data quality issues. That capability is now especially critical for any organization that is building or customizing artificial intelligence (AI) models, noted Whetten.

While software engineers have long used feature flags, also known as toggles, to test services, they are now also increasingly being used in production environments to limit access to specific tiers of capabilities and services that are made available under different licensing terms.

Data observability, meanwhile, has become a more pressing issue as the volume of data that applications need to access continues to increase exponentially.

As application environments become more complex, IT teams will increasingly be looking to standardize on platforms that provide a wider range of capabilities that go well beyond monitoring a set of pre-defined metrics, said Whetten.

The degree to which IT organizations will need to consume those services will, of course, vary widely. However, one certain thing is that application environments have become too complex to observe and manage without being able to access more advanced forms of analytics. The sheer volume of telemetry data being generated by modern applications is already overwhelming. The challenge now is to provide IT teams with an integrated set of services that enable them to identify and remediate issues long before they can have a material impact on application environments, noted Whetten.

There is, of course, already no shortage of options when it comes to observability platforms for analyzing telemetry data. The issue is making sure the return on investment in those platforms can be justified in a way that business leaders can easily appreciate.

May 22, 2025
Red Hat Previews Series of Updates to Ansible Automation Platform

Red Hat today at AnsibleFest 2025 previewed support for the Model Context Protocol (MCP) developed by Anthropic within Ansible Lightspeed, a set of generative artificial intelligence capabilities based on large language models (LLMs) specifically trained by Red Hat and IBM to automate IT tasks.

Any LLM or associated AI agent will soon be able to invoke AAP to execute a playbook. Additionally, IT teams can make use of Ansible policy enforcement based on Open Policy Agent (OPA) framework to establish guardrails for any AI agent or LLM accessing MCP.

Red Hat also previewed a customizable dashboard for the Ansible Automation Platform (AAP) that makes it simpler to surface the economic value created by an automated workflow. IT teams will be able to ascribe a value to an automation that will then generate a dashboard showing its value based on how often it runs. That dashboard can then be saved as a PDF file or embedded directly into a spreadsheet application that can be shared with finance teams to help evaluate the return on investment in automation.

Finally, Red Hat and HashiCorp previewed additional integrations planned with the Terraform infrastructure-as-code platform following the acquisition of HashiCorp by IBM, the parent company of Red Hat.

Sathish Balakrishnan, VP and general manager of the Ansible business unit within Red Hat, told conference attendees there will soon be more out-of-the-box integrations to enable automated workflows that include Terraform IaC tools that are widely used to provision IT environments. The two platforms together provide a highly complementary set of capabilities for automating IT workflows on a more end-to-end basis, he added.

HashiCorp CTO Armon Dadgar, meanwhile, added there will soon be more Terraform hooks into AAP to provide deeper integrations into production environments.

It’s not clear how many DevOps teams are looking to integrate Terraform with Ansible, but the opportunity to automate IT processes more fully presents an opportunity to streamline workflows. The challenge, as always, is aligning what are often different teams within the same organization.

Regardless of the approach, it’s clear that as more workflows are automated, the overall level of toil experienced by IT professionals should sharply decline in the months ahead—especially as AI models are more broadly applied—even as the number of applications deployed in production environments continues to exponentially increase.

It’s not likely those AI models and agents will replace the need for DevOps engineers and IT administrators any time soon. However, the nature of those roles is likely to profoundly change as more tasks are assigned to AI agents that are supervised by an IT team.

In the meantime, those IT teams would be well-advised to start identifying which manual tasks being performed today might soon be automated using a combination of Terraform tools and AAP that share access to the same core AI model and agent technologies. Truth be told, chances are high the bulk of that list will be made up of tasks that no one really enjoyed doing in the first place.

May 20, 2025
JFrog and Dynatrace Extend DevOps Reach to NVIDIA Enterprise AI Factory

NVIDIA in partnership with JFrog and Dynatrace are working toward integrating DevOps tools with a platform for building artificial intelligence (AI) agents.

At the COMPUTEX conference in Taiwan this week, NVIDIA added a validated design for building AI agents to the NVIDIA Enterprise AI Factory, an application development platform based on the NVIDIA Inference Microservices (NIM) framework that leverages containers to provide access to reusable services.

Additionally, NVIDIA also added NVIDIA AI Blueprints to make it simpler for developers to build avatars, otherwise known as smart AI teammates.

At the same time, JFrog revealed that its platform for managing software artifacts will be included in the NVIDIA Enterprise AI Factory platform.

JFrog CTO Yoav Landman said the Software Supply Chain Platform developed by JFrog will now serve as a single source of record for keeping track of the provenance of the software artifacts and AI models used to build AI applications. The JFrog Software Supply Chain Platform provides the ability to track the pulling, uploading and hosting of AI models and datasets, AI containers, Docker containers, and dependencies across NVIDIA’s AI Factory platform. All told now, the JFrog artifact repository can now support more than 40 different types of software packages, including NVIDIA NIM.

Additionally, DevSecOps teams building these applications will be able to use JFrog tools to scan for vulnerabilities and other security-related issues, he added.

JFrog also revealed the core JFrog Platform can now also run natively on NVIDIA’s Grace Blackwell graphical processor units (GPUs) to improve overall performance.

Dynatrace, meanwhile, announced it is integrating its observability platform with NVIDIA Enterprise AI Factory to enable DevOps teams to troubleshoot the NVIDIA application development platform.

It’s not clear how widely adopted the NVIDIA Enterprise AI Factory has been adopted, but as more AI applications are built, the need to integrate these types of platforms into DevOps workflows is becoming more pressing. On one level, AI models are just another type of artifact being used to build an application. However, the way existing artifacts are managed using version control systems doesn’t as easily apply to AI models. Each new version of an AI model, for instance, isn’t backwards compatible with the previous instance of the model, noted Landman.

Regardless of how AI application development becomes integrated with DevOps tooling and platforms, the pace at which these applications and associated AI agents are being built will increase dramatically in the age of AI.

The challenge and the opportunity is integrating a set of application development platforms that, in many instances, are adopted by data science teams rather than traditional application developers. In effect, data scientists are emerging as a new type of end user that DevOps teams will need to support.

Undoubtedly there will be culture clashes in the months ahead as organizations look to extend their existing DevOps workflows to govern the development of AI applications. In the meantime, however, DevOps teams would be well-advised to investigate how their organizations may be building AI applications today in a way that probably isn’t going to scale as easily as it should.

May 20, 2025
CloudBees Moves to Unify Management of Multiple DevOps Platforms

CloudBees today launched a software-as-a-service (SaaS) platform for centralizing the management of multiple DevOps environments, including GitHub, GitLab and the open source Jenkins continuous integration/continuous delivery (CI/CD) platforms.

Company CEO Anuj Kapur said CloudBees Unify extends the reach of CloudBees beyond Jenkins and will enable DevOps and platform engineering teams to centrally manage application development environments more easily via a control plane for CI/CD platforms through which DevOps teams can govern and manage multiple platforms.

Fragmented DevOps toolchains are a disservice to organizations because, in addition to slowing down the pace of application development, they also increase total costs, noted Kapur.

Some DevOps teams are trying to address that issue by standardizing on a single platform. However, application developers want to be able to innovate using the tools they prefer. CloudBees Unify provides a way to streamline DevOps workflows and processes while continuing to enable developers to work with any tool they prefer, said Kapur.

CloudBees Unify, in effect, provides an operating layer on top of any existing toolchain, based, for example, on GitHub Actions, he added. That capability makes it possible to both surface analytics in real time, run tests and apply policies that enforce running code scans and implement compliance policies, noted Kapur.

It’s not clear how many organizations are centralizing the management of DevOps workflows, but the rise of artificial intelligence (AI) tools for writing code will soon force the issue, said Kapur. The sheer volume of code moving through DevOps pipelines will soon reach levels that will be impossible to manage without relying on increased automation, he added. As the cost of writing code increasingly approaches zero in the age of AI, many DevOps teams will be simply overwhelmed, he added.

Each organization will need to determine for itself to what degree it may want to centralize the management of DevOps, but it’s not uncommon for enterprise IT organizations to have multiple different platforms performing the same essential functions. Each of those teams adopted DevOps practices at varying times, with each one often making different tooling and platform decisions. However, it soon becomes apparent that acquiring and deploying multiple tools and platforms requires levels of integration that, over time, increase the total cost of application development and deployment.

It’s not clear to what degree AI is being applied to application development, but a recent Futurum Research report found 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code, while 39% plan to make use of AI models based on machine learning algorithms. The one certain thing is that as AI tools are more pervasively adopted across the software development lifecycle, the overall pace of application development is only going to further accelerate.

Regardless of how any DevOps team moves forward, the one thing to remember is that more code doesn’t always directly translate to more applications being deployed without revisiting how pipelines constructed for a different era need to be either replaced or re-engineered.

May 20, 2025
Zencoder Previews AI Agents for DevOps Engineering Teams

Zencoder today revealed it is providing early access to artificial intelligence (AI) agents designed specifically to automate tasks across DevOps workflows running on continuous integration/continuous development (CI/CD) platforms based on open source instances of Jenkins or platforms from GitHub, GitLab, BitBucket, CircleCI and others.

Previously, Zencoder launched an AI platform for writing and testing code to provide integration with third-party DevOps tools such as JIRA, GitHub, GitLab and Sentry that can be tightly integrated into development tools such as VS Code and JetBrains.

Zencoder CEO Andrew Filev said the company is now extending that capability further with the development of autonomous AI agents that can resolve issues, implement fixes, improve code quality, generate and run tests, and create documentation. Use cases for these AI agents span everything from automated bug resolution using agents to analyze root causes and create fixes to scanning and developing patches for vulnerabilities.

Designed to be triggered using webhooks, each of these agents then submits the results of their efforts via a standard pull request that needs to be reviewed and approved by DevOps engineers, said Filev.

Additionally, DevOps teams can assign AI agents to keep track of the amount of technical debt that is building up as applications are updated, he added.

The overall goal is to not only eliminate much of the manual toil that conspires to slow down the rate of application development, but also reduce the amount of time that is otherwise required to onboard new members to a DevOps team, said Filev.

Zencoder already makes available a platform for building customizable agents using a mix of open source and proprietary large language models (LLMs) that can be shared via an open-source marketplace. At the core of those AI agents is a platform capable of analyzing interdependencies, generating documentation and suggesting improvements across service boundaries that Zencoder is now using to build agents trained to automate software engineering tasks.

The company also plans to expand the capabilities of these agents to interoperate with one another using the Model Context Protocol (MCP) developed by Anthropic in addition to providing analytics and administration tools.

It’s not clear how many organizations are relying on AI to help build applications faster. A recent Futurum Research survey finds 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code, while 39% plan to make use of AI models based on machine learning algorithms.

Eventually, the pace at which code is being created using AI tools will overwhelm existing DevOps pipelines and workflows so the need for AI agents to automate more tasks is rapidly becoming self-evident. In fact, there is already no shortage of AI tools and platforms for writing and testing code. Unfortunately, many organizations still lack a cohesive strategy, so many individual developers are naturally experimenting with different tools and approaches on their own. The challenge now is deciding which one of these tools and platforms best suits the needs of the organization versus the preference of a handful of individual developers.

May 19, 2025
GitLab Extends Scope and Reach of Core CI/CD Platform

GitLab Inc. has updated its core continuous integration/continuous delivery (CI/CD) platform to natively provide more artificial intelligence (AI) capabilities, along with built-in software artifact management capabilities and more robust security tools.

Justin Farris, vice president of product management for GitLab, said GitLab 18 makes available AI capabilities, such as code suggestion and completion, to all customers rather than requiring a separate GitLab Duo subscription.

There are still GitLab Duo capabilities that are not available in the core platform, but as DevOps continues to evolve, it is becoming more apparent which capabilities are going to be required by most customers compared to others that only a subset of GitLab customers may require, he added.

Additionally, GitLab is also moving to make GitLab Duo itself more widely accessible by no longer requiring customers to upgrade to GitLab Ultimate, the highest level of service that GitLab makes available on its software-as-a-service (SaaS) platform. Instead, organizations that have a GitLab Premium subscription can now also license GitLab Duo.

GitLab is also making it simpler to adopt best DevSecOps practices by providing built-in artifact management tools such as immutable tags and a virtual registry for Maven, open source project management software from the Apache Software Foundation (ASF).

There is also now support for structured inputs and enhanced pipeline management tools to manage artifacts and detect any changes made to them. Those capabilities promise to make it simpler to both securely parent/child pipelines and optimize pipeline execution. GitLab Query Language also now enables users to find, filter and embed content from anywhere in the GitLab platform.

GitLab has also added impact assessment tools for changes made to security policies, additional vulnerability dashboards, a reachability analysis capability for dependencies, support for passkey as defined by the FIDO Alliance and an ability to customize the detection logic it makes available in its static application security testing (SAST) tool.

Finally, GitLab 18 added more controls for complying with additional security frameworks, including the ISO 27001 framework for implementing information security management systems and benchmarks defined by the Center for Internet Security (CIS).

Collectively, these additional AI, artifact management and security capabilities ultimately serve to further reduce the total cost of DevOps by reducing the need for third-party offerings, while at the same time streamlining workflows, noted Farris.

It’s not clear how aggressively DevOps teams are streamlining workflows, but as more of them adopt platform engineering as a methodology for managing pipelines at scale, many of them are at the very least evaluating their options. At the same time, the rise of AI is also driving organizations toward platforms that make it simpler to extend the accuracy of a model by, for example, exposing it to the telemetry data they collect.

Regardless of approach, the one certain thing is that DevOps will continue to become more automated. The challenge and the opportunity now is determining which manual tasks performed today by a DevOps engineer lend themselves better to being assigned to an AI agent that has been specifically trained to complete them.

May 19, 2025