Derek Ashmore
Roswell
As seen in:
TechRadar,
TechRepublic,
CIO,
SiliconANGLE,
The New Stack,
SD Times,
Built In,
DATAVERSITY,
DevOps.com,
Isle of Wight County Press
and
I learn for a living. During the week, I take time out to use what I've learned for the benefit of my consulting clients.
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Derek Ashmore
AI Doesn’t Scale Without a Factory Floor
Across enterprises, platform teams are hitting the same wall: every new AI use case demands yet another bespoke environment, another GPU cluster with slightly different networking rules, another compliance review for the fifth time this quarter. It’s the same pain that once drove the rise of golden paths and internal developer platforms. AI workloads, especially multimodal and GPU-heavy ones, require too many moving parts for every team to start from zero.
Agentic AI breaks out of the lab and forces enterprises to grow up
Enterprises are making faster progress with agentic AI than many expected, not because the tooling is mature, but because companies have realized they can’t afford to wait. The leading 10 to 20% of organizations are racing ahead, standing up internal “agent platforms” that handle planning, tool selection, long running memory, workflow coordination, and human in the loop approvals. Capabilities they once assumed off the shelf copilots would provide.
AI Doesn't Scale Without a Factory Floor: Why AI Templates are Essential
Across enterprises, platform teams are hitting the same wall: every new AI use case demands yet another bespoke environment, another GPU cluster with slightly different networking rules, another compliance review for the fifth time this quarter. It's the same pain that once drove the rise of golden paths and internal developer platforms. AI workloads, especially multimodal and GPU-heavy ones, require too many moving parts for every team to start from zero.
4 Ways Platform Engineering Maturity Can Deliver Business Value
The clearest sign of platform engineering maturity is when the platform fades into the background. Developers stop worrying about pipelines, permissions and environments, and simply build. A mature platform provides seamless self-service options that automatically meet compliance and security requirements, freeing teams to focus on innovation rather than maintenance. When this happens, developer experience becomes the core performance indicator.
The Next CIO Imperative: Turning AI Chaos Into Competitive Clarity
With employees adopting AI faster than IT can regulate, CIOs must balance empowerment and control through smart, transparent governance. As generative AI tools flood the enterprise, CIOs are facing a new reality: employees are no longer waiting for permission to use AI. From marketing to finance to HR, workers are experimenting with ChatGPT-type tools to automate tasks and boost productivity. This shadow AI revolution — AI use outside IT’s formal oversight — creates both opportunity and risk.
When AI Starts Seeing and Hearing, IT Must Start Rethinking
In 2026, enterprises will find themselves navigating a seismic shift in AI. Gone are the days when text-only models ruled the landscape. The next wave is all about multimodal AI: Systems that read, listen, see and interpret the world just like we do. For IT leaders, this transformation is less about novelty and more about fundamental rewiring of the way work happens. But make no mistake: The infrastructure, governance and organizational demands are weighty.
How Multi-Agent AI Speeds Up App Modernization
Image: iLexx/Envato Modernizing legacy applications is a complex challenge. Companies contend with technical debt, scattered documentation, monolithic codebases, and business-critical systems that cannot be taken offline. Multi-agent AI orchestration frameworks such as Claude-Flow offer a new way to accelerate and de-risk cloud migration and application modernization.
Platform Teams: Stop Fixing Outages and Start Designing for Reliability - Platform Engineering
For years, platform engineers have lived in a constant state of firefighting. Pager alerts, late-night war rooms, emergency patches. These have been the rituals of teams charged with “keeping the lights on.” But today, reliability isn’t a side effect of hard work; it’s a discipline built through smart feedback loops, intelligent automation and a mindset shift.
Unleashing Shadow AI: Turning Hidden Risks Into Innovation
As generative AI tools flood the enterprise, CIOs are facing a new reality: employees are no longer waiting for permission to use AI. From marketing to finance to HR, workers are experimenting with ChatGPT type tools to automate tasks and boost productivity. This “shadow AI” revolution, AI use outside IT’s formal oversight, creates both opportunity and risk. When left unmanaged, it can lead to data loss, compliance violations, and reputational damage.
When AI Starts Seeing and Hearing, IT Must Start Rethinking Original
In 2026, the question for IT isn’t whether to adopt multimodal AI. It’s how fast they can do so without turning into chaos. In 2026, enterprises will find themselves navigating a seismic shift in artificial intelligence. Gone are the days when text-only models ruled the landscape. The next wave is all about multimodal AI: systems that read, listen, see, and interpret the world just like we do.
AI Literacy: Why IT Leaders Can’t Afford to Fall Behind
AI literacy is now essential for IT leaders to guide adoption responsibly, align with business goals and stay competitive in an AI-driven enterprise. AI is no longer a futuristic buzzword or a niche specialty reserved for research labs. It is now embedded in every corner of the enterprise, from cloud platforms and security operations to customer experience and software development.
Coding Agent Teams: The Next Frontier in AI-Assisted Software Development
It’s 2025, and coding agents are old news. It has become commonplace for programmers to work with an AI-powered agent, such as Copilot or Cursor, to help accelerate the software development process. Today, innovative programmers are taking AI agents a step further. They’re building entire teams of coding agents, an approach that doubles down on the efficiency and scalability of AI-assisted coding tools.
4 Common Causes of Agentic AI Implementation Failure
Image: Shutterstock / Built In Summary: AI agents are gaining traction in business, but adoption often fails due to unrealistic expectations, poor use case prioritization, bad data quality and weak governance. Success depends on clean data, clear goals, human oversight and strong guardrails. more AI agents are gaining traction in business, but adoption often fails due to unrealistic expectations, poor use case prioritization, bad data quality and weak governance.
When AI agents go rogue: why IT can’t afford blind trust
Although it’s unclear exactly what went wrong in that case – whether it was an issue like a buggy AI agent, a poorly written prompt or a failure on the part of the LLM that guided the agent’s actions – the incident highlights the types of severe problems that can occur when AI agents do things they shouldn’t do. By extension, it underscores the critical importance of taking steps to manage the risks that AI agents pose.
Enterprise IT is undergoing a foundational transformation. The age of passive automation, scripts triggered by humans and bots that follow fixed logic, is giving way to something far more profound. Agentic AI, systems that operate with intent, memory and reasoning, are becoming autonomous decision-makers. These agents do not wait for commands; they detect, decide and act across environments. As they embed themselves into infrastructure, workflows and security protocols, they are no longer tools.
4 Reasons Agentic AI Is Failing
There are plenty of reasons for businesses to adopt AI agents, which can boost productivity and reduce employee tedium. There are also plenty of ways in which AI agent implementations can fail — and as more and more organizations move to adopt agentic AI, the smart ones will think proactively about how to get ahead of the pitfalls that could cause agentic projects to go awry.
Best Practices for Managing AI Agents in Production
AI agents can do amazing things to make the lives of IT professionals easier. But, they also have the potential to do amazingly bad things — a lesson some organizations are already learning the hard way. For instance, Replit, a provider of software development tools, announced in July 2025 that an AI tool had deleted an entire production database.
Your Infrastructure Has a Mind of Its Own Now
Enterprise IT is undergoing a foundational transformation. The age of passive automation, scripts triggered by humans and bots that follow fixed logic, is giving way to something far more profound. Agentic AI, systems that operate with intent, memory, and reasoning, are becoming autonomous decision-makers. These agents do not wait for commands; they detect, decide, and act across environments.
Rebuilding the Workforce Around AI and Automation Original
Organizations are struggling to fill highly specialized AI positions. The result is a workplace caught between acceleration and stagnation, progress and displacement. In a year marked by economic uncertainty and hiring freezes across much of the tech industry, one trend is moving rapidly in the opposite direction. The demand for AI and automation talent is not just surviving the slowdown; it is thriving.
Who’s Really In Charge Of The Digital Workspace?
In a workplace increasingly saturated with tools for productivity and collaboration, the most disruptive shift isn’t just a new platform, it’s a new kind of co-worker. Agentic AI is redefining the boundaries between tool and teammate, raising a provocative question: when software starts acting independently, who’s really running the digital office? Agentic AI models represent a dramatic departure from traditional bots and scripted integrations.
DevOps won’t scale without platform engineering and here’s why your teams are still stuck Original
Despite a decade of DevOps fervor, most engineering organizations remain hindered by manual processes, silos, and dependency bottlenecks. Teams cannot truly own their delivery stack and still depend on centralized support for deployment, provisioning, and security. The missing piece in achieving real, sustainable DevOps autonomy is platform engineering.
5 Challenges DevOps Must Solve to Prepare for AIOps
As DevOps practices evolve, teams increasingly turn to AIOps (AI applied to IT operations) to drive faster incident detection, smarter analytics and self-healing automation. Though AIOps can dramatically augment DevOps capabilities, integrating intelligent systems requires overcoming complex technical, procedural and cultural hurdles. This article explores five pressing challenges and offers practical strategies to address them.
How IT Pros Can Prepare For AI-Driven Application Modernization
In an era defined by constant technological evolution, businesses face increasing pressure to modernize their applications. Legacy systems, while foundational to enterprise infrastructure, can no longer keep pace with demands for scalability, agility, and innovation.
Why and How to Enhance DevOps with AIOps
AIOps, the practice of enhancing IT and DevOps with help from artificial intelligence and machine learning, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a way to speed up monitoring. AIOps in conjunction with modern generative AI technology allows DevOps teams to do much more.
Why and How to Enhance DevOps with AIOps
AIOps is certainly not a wholesale replacement for traditional DevOps tools and processes, but it is an enabler of major boosts in efficiency for DevOps teams. AIOps, the practice of enhancing IT and DevOps with help from AI, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a way to speed up monitoring.
Why and How to Enhance DevOps with AIOps
AIOps is certainly not a wholesale replacement for traditional DevOps tools and processes, but it is an enabler of major boosts in efficiency for DevOps teams. AIOps, the practice of enhancing IT and DevOps with help from AI, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a way to speed up monitoring.
How to Supercharge Application Modernization with AI
You probably know that AItools like Copilot and CodeWhisperer are quite good at writing applications. That’s great if you’re developing a new app from scratch. But what if you have an existing app — one you may have developed years ago, long before AI-assisted coding was a thing — that you’d like to modernize. Can AI help you there, too?
Best practices for CI/CD migration: The GitHub Enterprise example
Continuous Integration/Continuous Delivery (CI/CD) software – meaning solutions that teams use to build, test, and deploy applications – has come a long way over the past decade. Whereas organisations once cobbled together CI/CD pipelines using disparate open source tools, they now have a plethora of end-to-end, vendor-supported enterprise CI/CD platforms that they can use instead. Because these solutions offer everything teams need to deliver software, they are simpler to deploy and manage.
A Guide to Generative AI for DevOps Team Managers
Over the past couple of years, there has been extensive discussion of how generative AI can benefit DevOps practitioners. The ecosystem has been abuzz with discussions of leveraging GenAI to accelerate virtually all aspects of day-to-day DevOps work, from coding to software testing to documentation generation. A topic that has received much less attention is how DevOps team managers — instead of practitioners — can use generative AI.
A Guide to Generative AI for DevOps Teams
There has been copious talk over the past couple of years about how DevOps teams and practitioners can benefit from generative AI. The ecosystem has been abuzz with discussions of leveraging GenAI to accelerate virtually all aspects of day-to-day DevOps work, from coding to software testing to documentation generation and beyond. A topic that has received much less attention is how DevOps team managers – as opposed to practitioners – can use generative AI.
Putting GenAI to Work in DevOps: A Practical Guide
The emergence several years ago of generative AI software engineering tools, such as GitHub Copilot, spawned tremendous speculation about how the DevOps teams of the future would leverage AI to accelerate workflows and gain efficiency. Now, more than three years after genAI exploded onto most DevOps practitioners’ radar screens, the days of speculation are over, and the era of real-world AI use for DevOps has begun.
Best practices for CI/CD migration: The GitHub Enterprise example
Continuous Integration/Continuous Delivery (CI/CD) software – meaning solutions that teams use to build, test and deploy applications – has come a long way over the past decade. Whereas organizations once cobbled together CI/CD pipelines using disparate open source tools, they now have a plethora of end-to-end, vendor-supported enterprise CI/CD platforms that they can use instead. Because these solutions offer everything teams need to deliver software, they are simpler to deploy and manage.
Best Practices for CI/CD Migration: The GitHub Enterprise Example
Continuous integration/continuous delivery (CI/CD) software — meaning solutions that teams use to build, test and deploy applications — has come a long way over the past decade. While organizations once cobbled together CI/CD pipelines using disparate open-source tools, they now have a plethora of end-to-end, vendor-supported enterprise CI/CD platforms that they can use instead. Because these solutions offer everything teams need to deliver software, they are simpler to deploy and manage.
5 Best Practices for Getting the Most from Platform Engineering
Choosing to increase developer productivity and improve the developer experience by adopting platform engineering is easy. What’s much harder is implementing platform engineering capabilities that actually deliver their intended value. That’s because a variety of challenges can arise in the context of platform engineering. At best, these challenges make platform engineering solutions less effective than they would ideally be.
Security, Automation and Developer Experience: The Top DevOps Trends of 2024
If you ask most folks to describe the top DevOps trends in 2024, you’ll likely hear buzzwords like AI or DevSecOps. Those are certainly trendy topics. But based on my everyday work helping businesses plan and execute DevOps strategies, I’m noticing a different set of salient trends in DevOps. Although much is being said about how technologies like AI might impact DevOps, the biggest changes I’m seeing right now involve other types of solutions and techniques.
Security, automation and developer experience: The top DevOps trends of 2024
If you ask most folks to describe the top DevOps trends in 2024, you’ll likely hear buzzwords like AI or DevSecOps. Those are certainly trendy topics. But based on the work I do on an everyday basis helping businesses plan and execute DevOps strategies, I’m noticing a different set of salient trends in the world of DevOps. Although much is being said about how technologies like AI might impact DevOps, the biggest changes I’m seeing right now involve other types of solutions and techniques.
Here Are 5 Ways to Get the Most Out of MLOps
Taking an AI model from the design phase all the way through to production deployment is a complicated and tedious process; this is why MLOps, which streamlines model development and management, is so valuable for any organization that wants to excel at AI and machine learning. The better that teams implement MLOps, the faster, more reliable and more productive their AI workloads will be.
5 Common Developer Self-Service Challenges (and Solutions)
How do you enable developer self-service? That’s a question that more enterprises are seeking to answer as they embrace developer self-service — or platform engineering, as it’s sometimes called — as a means of maximizing the productivity and job satisfaction of their software engineers. Developer self-service represents the next evolutionary step for organizations that have already embraced DevOps since it helps their development teams to make DevOps practices more efficient.
AIOps Archives | RT Insights
Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.
Safely Testing Applications in Production
There are many different types of application testing, such as unit tests, integration tests, performance tests, availability tests, and many more. This article is entirely about system-level testing, the most outer-level user experience testing. For example, with a web application, system-level testing is testing from an end-user perspective. System-level represents what’s most important from a functional standpoint as it verifies that users can do business.
5 Best Practices for Deploying Kubernetes
Kubernetes is like pizza. There is a virtually endless variety of ways to configure it, and the approach you take determines whether you end up with an optimal hosting environment or the software equivalent of kiwi-and-mozzarella pie. I'm not here to tell you what to put on your pizza. What I would like to do, however, is explain five of the most important—and most easily overlooked—best practices for configuring a Kubernetes environment.
Five ways to tell whether Kubernetes is a good fit for your app
In many tellings, the path to application modernization goes like this: First, you refactor your application into microservices. Next, you containerize each service. Finally, you deploy it on Kubernetes, the open source orchestration engine that has become the de facto platform for running containerized apps. The thing is, not all modernization stories follow this narrative. In many cases, Kubernetes isn’t – and shouldn’t be – part of the journey to modern software deployment.
All Monoliths Are Not Created Equal
All monoliths are not created equal. That is, not all monoliths became monoliths the same way nor do they have the same root causes. With this post, I'll identify different types of monoliths I've seen. This is useful as the tactics I use to break the monolith into smaller, more manageable, pieces is different depending on which category of monolith I'm dealing with. It's worth noting that some times elements of these different categories of monoliths are used in combination.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Derek
Contact Derek, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack