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Tag: automation

Gain Control of Your Innovation With Low Code Technologies

As an entrepreneur and CEO of a large global company, I once believed that building an exceptional business technology infrastructure meant creating it entirely from scratch. To me, software coding and development were processes that demanded immense specialized skill, consumed significant time and incurred substantial costs — precise, yet never agile.

That’s why I was so impressed to discover low-code technology — a sophisticated approach to software development for applications and systems, which delivers remarkable speed and flexibility. It can be implemented with minimal hand-coding, leveraging visual interfaces and drag-and-drop functionality, enabling professionals with limited technical expertise to build applications more efficiently.

Thanks to low-code technology, you can assemble unique systems using high-quality, pre-engineered components, much like advanced Lego bricks. Instead of working from scratch, you simply snap together ready-made components to define workflows and connect various elements of a system, even adding custom code when needed to adapt to your business needs.

Studies suggest that low-code technology can help achieve up to a 90% reduction in development time compared to traditional coding methods. Again, you don’t need to know how to make the Legos — just how to put them together.

Our global team at RunMyProcess, a cloud-based platform for low-code application development, builds robust business tools — customer portals, internal dashboards and tailored workflows — in days or weeks, not months. Our experts quickly bring solutions to life, freeing clients’ teams from complex challenges.

This approach also allows for broader participation, enabling more team members to contribute to progress through technology, driving innovation and fostering deeper collaboration. More importantly, low-code technology empowers businesses to turn ideas into functional applications, bringing products and services to market faster. You no longer have to risk your IP while navigating a complex, time-consuming process to reach the market. With low-code technology, you can transform an idea into a reality faster than ever before.

Today, having a collection of brilliantly built elements is one thing, but making them work together seamlessly is another. That’s where orchestration transforms operations. Envision your entire business as a grand orchestra, with distinct sections such as sales, finance and operations, each playing its part in sync.

Without a conductor, each section might play its part independently, which means not performing as efficiently or productively as possible. A customer order comes in, the sales section acts and then someone manually cues finance, followed by operations. This chain of manual handoffs often results in delays and errors.

In real terms, effective orchestration relies on software platforms like RunMyProcess to automate and coordinate complex tasks across systems and departments. Our platform seamlessly merges human and AI data into a torrent of information. It eliminates wasted time and reduces errors that arise from manual coordination, making operations faster and more accurate.

Forget the intimidating jargon surrounding orchestration’s elaborate architectural patterns and other intricacies. At its core, the technology automatically ensures that every instrument, department and software system plays its part perfectly and in sequence.

Our high-performance cloud platform has helped major retailers, manufacturers, insurance companies, agribusinesses and service providers streamline their processes, improving both speed and efficiency.

For example, a publisher burdened by manual edits, lost files and endless emails used the RunMyProcess DigitalSuite to automate workflows. This eliminated 200 hours of weekly busywork, cut article turnaround time in half and gave editors more time to focus on content quality.

Similarly, a global farm machinery manufacturer overcame slow, error-prone paper inspections by using RunMyProcess’ platform to create a custom digital check sheet. Within weeks, it accelerated quality control by 30%, improved inspection accuracy and gained real-time access to production data — enabling faster fixes and more consistent quality.

Bringing together the two tools creates a powerful synergy: Low-code accelerates application creation, and orchestration ensures those applications integrate smoothly across the entire organization. Ultimately, that combination helps companies control costs and manage complexity. While I’ve seen firsthand how these changes make a real difference, as with any construction project, thoughtful planning and skilled integration are essential to ensure everything fits and functions. Lego bricks and orchestra conducting work well as simplified images, but real-world integration is often more complex.

Still, what truly stands out is how collaboration improves. It’s not just the tech folks at the forefront — people across the business can contribute. Processes become clearer, and it’s easier for everyone to get involved.

In the end, fusing low-code and orchestration is more than a technical upgrade. I see it as a business imperative and an empowering shift that is reshaping how leading companies get work done, and who takes the lead. It creates a foundation for resilience, agility and profitability — turning today’s innovations into tomorrow’s advantages.

November 5, 2025
How I Automated My Most Tedious Task and Unlocked Our Growth Strategy
Automating the Tedious: Unlocking Our Growth Strategy

In my role, I analyze data and guide strategies, without getting lost in the weeds of manual data entry. However, a significant part of my workflow involved just that. We had a crucial data source that gave us a starting point — a list of company domains — but it lacked the rich firmographic and technographic details required for meaningful analysis.

The Challenge: The ~400-Record Monthly Grind

The process was painfully manual and inefficient. For every single record, I had to search on the website for the company’s industry, funding status, location and recent news. This wasn’t a small task; we process around 400 records every month. The time spent on this repetitive cycle was immense, preventing me from doing the high-impact work I was hired for. I wasn’t analyzing data; I was just creating it.

This bottleneck was more than an annoyance; it was a strategic drag. I was looking for a way to automate the enrichment process and reclaim my time for analysis and action.

The Solution: An AI-Assisted Development Journey

I believe there’s a right way and a wrong way to use AI. The wrong way is to treat it like a vending machine: To open a chatbot and say ‘Make me a marketing plan’ and blindly go with the results.

The right way I’ve found is to think of Al as a sous-chef. It can prepare ingredients at high speed, handling the grunt work, but you’re the head chef who creates the recipe and tastes the final dish. Your job is to be skeptical, constantly refining what it provides. That back and forth is the part of the process that can tap into your creative process.

Step 1: Defining the Vision With Gemini

I started with a clear overview of what the application should do. Later, I worked with Google Gemini to expand on these ideas and define the more intricate details. This collaborative process was invaluable. It helped me think through complex scenarios I might have otherwise missed, like sophisticated error handling. For instance, what happens if an enrichment process is paused? How should it resume? Tackling these questions early on saved me from major headaches down the line.

Step 2: Building the Code With Cursor.ai

With a detailed project brief in hand, I turned to Cursor.ai, an AI-powered code editor. While I can do some coding, building a robust, production-level application from scratch was beyond my immediate skill set. Using the detailed outline I created with Gemini, I was able to use Cursor.ai to generate the foundational code for the project.

This process was a fantastic opportunity to hone my skills. I gained hands-on experience with a Node.js backend, a React frontend, connecting to a HubSpot MCP server, implementing batch processing for large files, and, most importantly, continuously improving the user experience by testing the app repeatedly.

How the CSV Enricher Works

The final product is a streamlined application that transforms a tedious manual task into a simple, automated workflow.

1. Upload & Parse: The user drags and drops a CSV file into the web interface. The back end immediately parses the file, deduplicates domains and prepares for enrichment.

Figure 1: Prompting the user to review the sample enrichment before updating all records.

2. Enrichment & Quality Check: The application takes the domain name from each record and uses a weighted search across various sources to find the company’s proper name, funding news, headquarters and other recent events. To ensure accuracy, it processes the first five records and then pauses, prompting the user to review the quality of the enriched data. This ‘human-in-the-loop’ step allows for tuning before committing to the entire file.

3. Finalize & Act: Once the enrichment is complete, the user has two choices:
- - Download: Export the newly enriched data as a CSV file that can be used in other tools
  - Sync to HubSpot: Automatically update company records in HubSpot via the MCP server integration, pushing the new data directly where it’s needed most.
Figure 2: Data has been enriched, and is ready for downloading or updating directly via the hubspot-mcp-server.

The Impact and The Wins: From Data to Strategy

What amazes me most about this project is not just the final tool but the empowerment that comes with it. By freeing myself from the repetitive, low-value task of manually updating spreadsheets, I can now focus on what truly matters: Taking action on the data.

This has led to significant, tangible wins for the business:
- Uncovering Actionable Answers: The application allows me to seamlessly work with mismatched datasets, finally connecting the dots. For the first time, we can visualize our user base by industry type. This was impossible when the industry field was just a blank column in a spreadsheet.
- Informing Key Strategy: We now have a clear, data-backed picture of which industries are leveraging our software. This insight is a game-changer, allowing us to build highly targeted industry-specific and account-based marketing (ABM) plans.
- Empowering Sales: This automated enrichment process directly empowers our sales team. They can now connect with companies that perfectly match our ideal customer profile (ICP), making their outreach more relevant and effective, which in turn drives real growth.
If you’re battling a similar bottleneck, start small, stay curious and don’t be afraid to experiment. If you’re a VictoriaMetrics user, check out our MCP server — it’s built to make enrichment as effortless as possible.
November 3, 2025
How Data, Empathy and Visibility Are Redefining DevOps Maturity

Amy Carrillo Cotten shares her perspective on how DevOps teams are evolving in an era defined by AI acceleration, rapid release cycles, and shifting engineering priorities. Drawing from her experience leading engineering transformation initiatives, Cotten emphasized that while automation continues to drive efficiency, true DevOps maturity still depends on people, processes, and the right mindset.

Cotten noted that many organizations are still struggling to balance speed and quality. The drive to ship code faster often outpaces efforts to measure impact or ensure that teams have the skills needed to validate AI-assisted development. She pointed out that engineering success isn’t just about writing more code — it’s about understanding how that code contributes to business outcomes and user experience.

The discussion also touched on the widening skill gap as AI and automation reshape development workflows. Cotten highlighted that leaders must focus not only on reskilling technical teams but also on creating a culture of continuous improvement — one that integrates data-driven insights into how engineers collaborate, communicate, and measure productivity.

She also underscored the importance of operational visibility across the DevOps lifecycle. Without unified insights into performance metrics and workflow health, teams risk falling back into silos that undermine agility. By building stronger feedback loops between developers, QA, and operations, organizations can ensure that innovation doesn’t come at the expense of reliability.

Ultimately, Cotten said, the future of DevOps will hinge on empathy, transparency, and discipline. Technology may continue to evolve, but sustainable delivery still depends on teams that understand how to move fast — responsibly.

October 31, 2025
How DevOps Is Evolving for the Age of Intelligent Automation

At swampUP 2025 in Napa Valley, Alan sat down with Vishal Raina and Shibashis Mishra to discuss how DevOps is adapting to a new era defined by artificial intelligence, automation, and digital transformation at scale. As organizations embed AI across their software delivery pipelines, the conversation has shifted from simple productivity gains to questions of governance, trust, and long-term sustainability.

They discuss the growing need for DevOps teams to balance rapid innovation with responsible automation. While AI and machine learning tools have made it easier to generate, deploy, and test code, they’ve also introduced new challenges — from security risks and model drift to explainability and regulatory compliance. The key, they noted, lies in integrating responsible AI practices directly into existing DevOps workflows rather than treating them as separate initiatives.

They also explore the evolution of intelligent automation and its impact on modern engineering culture. Traditional DevOps principles of speed and collaboration are being redefined by AI systems that learn, adapt, and sometimes act autonomously. To keep pace, teams are building new layers of observability, policy enforcement, and model lifecycle management — effectively creating an “AI-ready DevOps” discipline that blends governance with scalability.

One message was clear: the future of DevOps isn’t just about moving faster; it’s about moving smarter. By embedding ethical design, transparency, and accountability into every stage of the pipeline, organizations can unlock the full potential of AI without compromising stability or security. As AI-driven development continues to accelerate, the next evolution of DevOps will be measured not only by deployment frequency but by how well it maintains control, compliance, and trust across increasingly intelligent systems.

October 23, 2025
The Cloud Reset: DevOps Brings Workloads Back to Private Clouds

Many DevOps teams are hitting the reset button and rebalancing their use of public versus private clouds after a decade of “public cloud-first” strategies.

Recent research reveals that 69% of enterprises are considering moving workloads from public cloud back to private environments. DevOps teams’ desire for greater control, security and cost predictability is largely driving this shift. Indeed, 92% of IT leaders say they trust private cloud more for security and compliance needs than public cloud

The shift to private cloud is very DevOps-driven. This is because all stakeholders can benefit. DevOps teams benefit from the private cloud’s stability and how existing or future investments in on-premises infrastructure are complemented and maintained. With the benefits cloud computing offers (automation, scalability and self-service), DevOps teams are better able to manage security and meet compliance needs, especially when data sovereignty is concerned. Additional benefits for the business DevOps stakeholders include how private cloud can help reduce rapidly rising public cloud costs.

Learning from a Decade of Public Cloud

The lessons learned in the public cloud space over the past few years are key. Public cloud platforms are agile and scalable, but they often present unpredictable costs and data governance issues. Most companies admit that at least some of their public cloud budget is wasted, and almost half believe that at least a quarter of their cloud spending is lost to inefficiencies.

Cost unpredictability has surfaced as a major concern. Most companies acknowledge that at least some of their public cloud budget is wasted, and nearly half believe that at least a quarter of their cloud spending is lost to inefficiencies. What began as a promise of “pay only for what you use” often led to surprise bills from unforeseen usage spikes, data transfer costs and complex pricing models that made budgeting nearly impossible.

Equally significant are concerns over security and data control. While public cloud providers offer robust security features, many organizations struggle to navigate the shared responsibility model and the complexities of managing security across multiple cloud environments. More leaders believe that private or hybrid cloud environments better meet stringent security requirements and provide them with direct oversight of critical data. For industries with strict compliance mandates, having complete control over data location and access has become non-negotiable.

The Evolution, Not Abandonment, of Cloud Strategy

This shift doesn’t signify a failure of cloud computing or a return to traditional on-premises infrastructure. Instead, it illustrates a maturation of cloud strategy. Organizations seek the same cloud benefits, such as automation, self-service provisioning and scalability, but within a more predictable and accountable operational model.

Private clouds have advanced considerably since the early days of virtualization. Modern private cloud platforms now provide the same cloud-native capabilities that made public clouds appealing: containerization, microservices architecture, DevOps tooling and infrastructure as code. The distinction lies in where these capabilities are deployed and who oversees them.

Strategic Workload Placement

The trend toward repatriation is fundamentally about strategic workload placement. Organizations are realizing that not all workloads are equal, and the “lift and shift everything to public cloud” strategy often fails to yield the expected benefits.

Workloads with predictable resource requirements, sensitive data, or stable performance needs are increasingly being transitioned back to private cloud environments, where they can be managed and controlled more effectively. Meanwhile, workloads that benefit from elastic scaling, global distribution, or specialized cloud services continue to reside in public clouds.

This hybrid approach enables organizations to optimize for different priorities, keeping mission-critical applications in private clouds for security and control, while utilizing public clouds for innovation and experimentation. It’s about selecting the right tool for each task rather than forcing all workloads into a single model.

Cost Governance and Predictability

In private cloud environments, IT departments can allocate resources per workload and precisely determine the cost of delivering those resources. This predictability encompasses not only infrastructure costs but also operational expenses, compliance costs and the total cost of ownership.

When a department’s application suddenly requires additional compute power in a private cloud, it prompts a conversation about capacity planning and budget allocation, but not an unexpected bill at the end of the month. This level of control enables organizations to more effectively align IT spending with business objectives and mitigate the “shadow IT” issue that often plagues public cloud deployments.

The Path Forward

In this era of cloud reset, DevOps teams are not abandoning the benefits of the cloud, but rather refining their approach. Our analysis at ReveCom shows the next phase in cloud computing for DevOps teams involves selecting the right environment for each workload while ensuring performance, interoperability and accountability are in alignment with DevOps.

For many organizations, this means developing sophisticated hybrid cloud strategies that leverage the best of both worlds. Private clouds offer the control, security and predictability required for core business applications, while public clouds provide the flexibility and innovation platform for new initiatives.

As DevOps teams continue to move to private and hybrid clouds, it’s a strategic shift in how they think about their infrastructure. It’s acknowledging that the cloud isn’t just a destination, but an operational approach that can be applied where it makes the most sense for business. As DevOps teams continue to refine their cloud strategies, the focus is shifting from “cloud-first” to “cloud-smart,” with private clouds increasingly playing a crucial role in delivering the promise of cloud computing while maintaining the governance and control that enterprises need.

October 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.

Indeed.com

OpenAI
New York City
Client Platform Engineer
$255,000 to $325,000

Dice.com

Dell Technologies
Austin, TX
Principal Software Engineer
$170,000 to $220,000

LinkedIn

Jasper
Remote
DevOps Engineer
$170,000 to $200,000

The Job Network

CTG
Columbia, MD
DevOps Engineer
$120,000 to $226,000

SimplyHired.com

Viasat
Carlsbad, CA
DevOps Engineer
$111,500 to $208,000

October 20, 2025
Why Automation Fails Without the Right QA Mindset
In the DevOps world, with its faster release cycles and continuous delivery, quality assurance (QA) cannot be sidelined until the end of the process. Companies that actively use automation achieve up to 60% faster release cycles compared to those relying solely on manual testing. However, automation alone is insufficient. Without the right QA mindset, moving faster can easily backfire if quality is not built into every stage. QA teams must evolve to become quality enablers throughout the software delivery cycle. Rather than joining at the tail end of the lifecycle, they must work alongside product owners to understand functional requirements, collaborate with architects and developers to grasp solution design and ensure visibility into end-to-end solution delivery.

Getting involved more deeply from day one means QA can help build quality from the outset through deployment. In response to this shift, QA professionals need to step up by:
- Designing and managing automated tests in continuous integration/continuous delivery (CI/CD) pipelines
- Focusing on risk-based testing
- Collaborating with teams to ensure quality is integrated into every step of the delivery process
In a fully automated pipeline, QA teams add the most value when they go beyond just testing and contribute as collaborative partners, strategic advisors and critical thinkers throughout the entire development process.

The Importance of the Right QA Mindset

Automation is key to fast, continuous delivery, but its real impact depends on the mindset driving it. In fact, 63% of organizations deploy code without thoughtful testing in pursuit of faster delivery timelines. The so-called developmental mindset focuses solely on speed, code coverage and scripting mechanics — often resulting in a check-the-box mentality, where tests verify expected outcomes but overlook real-world usage, potential risks and user experience.

The QA mindset, by contrast, offers a broader, more critical perspective. It is rooted in exploration, skepticism and curiosity. As QA professionals, we don’t just ask whether the software works — we ask how it might fail, where it could break and whether it fulfills the user’s ultimate needs. This mindset is essential for prioritizing automated tests based on business or user risk, rather than simply following functional decomposition.

With this strategic mindset, we design automated tests that anticipate edge cases, test assumptions and reflect actual user workflows. This leads to earlier detection of defects, fewer production incidents and, ultimately, a smoother user experience. In the long run, it also improves team productivity and reduces rework and reactive firefighting by catching issues in advance.

Automation alone doesn’t guarantee quality — it is only as effective as the tests it is scripted to run. If the requirements are misunderstood, automated tests may pass while critical issues remain undetected. I have seen failures where teams relied solely on automation without involving proper QA practices, leading to tests that validated incorrect behavior. Automation frequently fails to detect new or unexpected issues introduced by system upgrades. It often misses critical problems such as faulty data mapping, incomplete user interface (UI) testing and gaps in test coverage due to outdated scripts.

Lack of adaptability is another common obstacle that I’ve repeatedly seen undermine automation testing efforts. When UI elements are tightly coupled, even minor changes can disrupt test cases. With the right QA mindset, this challenge is anticipated — promoting modular, maintainable automation strategies capable of adapting to frequent UI and logic changes.

Automation lacks the critical analysis required to validate business logic and perform true end-to-end testing. From my experience, the human QA mindset proved essential during the testing of a mortgage loan calculation system. While automation handled standard calculations and data validation, it could not assess whether the logic aligned with real-world lending rules. Here is where human insight was crucial: Automated tests confirmed data processing and standard calculations, but couldn’t verify whether the outputs complied with evolving lending regulations and complex business requirements. QA ensures that test scenarios are aligned with business goals and remain adaptable to change.

As QA professionals, we often serve as a bridge between developers and business teams — translating business requirements into actionable testing scenarios and ensuring the team’s efforts focus on what truly matters.

Why Human QA Thinking is Irreplaceable

Automated scripts can only validate what they have been explicitly designed to check. That is why human-driven testing remains essential, even in highly automated pipelines. Human-driven testing is especially valuable for UI and user experience (UX) validation, exploratory testing and identifying edge cases that automation may miss.

For example, in a human capital management payroll system upgrade project with zero downtime, we applied a hybrid testing strategy that combined automated and manual efforts. Automated regression scripts ensured core functionality remained stable, while human-driven tests were used to validate new scenarios and feature upgrades. This ensured proper integration with other modules and subsystems. The result was a fast, stable CI/CD pipeline that still allowed space for creativity.

Human insight remains a critical part of both DevOps and QA. Without a mindset grounded in strategy, risk awareness and a deep understanding of how real users interact with software, automation can mislead teams. Quality teams don’t just bring tools — they bring perspective. By asking the hard questions, they ensure testing efforts stay aligned with what truly matters to both the business and the end user.

Key Tips to Strengthen Your QA Mindset

In conclusion, here are the key QA mindset principles for successful test automation:
- Integrate QA from the start. Involve QA early in the process — don’t wait until the end.
- Adopt a risk-based, user-focused approach. Prioritize testing based on business impact and user risk. Go beyond validating expected outcomes — explore how the software might fail in real-world scenarios.
- Design maintainable and adaptable test automation. Avoid brittle scripts that break with every minor change.
- Blend automation with manual testing. Let automation handle data and calculations, and human testers cover new features, UI/UX and edge cases through exploratory testing.
September 16, 2025
The Quantum Shift Is Here: A Survival Guide for the New Era of Software

The job of a modern software professional has changed. It now seems to include everyone else’s.

Overnight, developers, security professionals and DevOps engineers have been asked to become cloud architects, AI whisperers, security sentinels and compliance officers. This isn’t just “shifting left.” It’s a total gravitational collapse where the entire business—its innovation, its revenue, its risk—now orbits the software supply chain. This new reality is the Quantum Shift, a fundamental change that demands new thinking. This is the central challenge we are taking head-on at swampUP 2025.

The Unsustainable Weight of Complexity

The pressure on engineering teams stems from the explosion of tools and methodologies we’re experiencing. We’ve passed a threshold in the movement from monoliths to microservices, increasing the number of components these teams manage by orders of magnitude. Each component has its own lifecycle, its own dependencies, its own vulnerabilities. To manage this, teams now operate a sprawling ecosystem of systems for source control, continuous integration (CI), security scanning, and artifact management (with Artifactory of course!). The cognitive load of constant context-switching is immense.

At the same time, security and compliance demands have become relentless. Modern applications are primary targets. Development and security teams are on the front line, expected to generate SBOMs, verify the integrity of every open-source package, and provide evidence of secure practices. An auditor’s request can halt development for days as they manually piece together data from a dozen different systems. This is an inefficient and frustrating use of their engineering skills.

Why Throwing More Tools at the Problem Fails

The instinctive response to this complexity has been to buy more tools. A new scanner here, a separate workflow tool there. But this approach can only, obviously, make the problem worse. Each new tool is another dashboard to check, another silo of data, or another integration point that can fail. Teams lack a single source of truth. They cannot see the full lifecycle of a component from a developer’s first commit to its final deployment. This gap in visibility is a major source of risk and inefficiency. Engineers spend more time managing the tools than creating value for their customers.

The Human Element in a World of Automation

The Quantum Shift isn’t just about tools; it’s about the role of the modern engineer. There’s a palpable fear in our industry about automation and AI taking jobs, so let’s confront that. I don’t believe these tools are here to replace software professionals. They are here to amplify them. Think of the leap from hand tools to power tools. A master carpenter with a handsaw was a craftsman. But with power tools, that same person could build a house. The craft wasn’t lost; the scale of impact was magnified.

This is our power tool moment. The professionals who master this new reality—who learn to manage complexity and leverage automation—will become the new leaders, delivering the impact of an entire team.

The Case for a Unified Platform

The solution is a unified platform, a central system of record for every software component. This single source of truth gives teams complete visibility, allowing them to trace a vulnerability from production back to a specific line of code in moments. It automates security and compliance checks within the workflows their developers already use, reducing their cognitive load.

This unified approach requires a strong ecosystem. At swampUP, we’re assembling an alliance of the industry’s brightest minds to show how it’s done. Attendees will hear from leaders at NVIDIA, ServiceNow, Sonar, and GitHub, all focused on a single mission: providing a real-world roadmap for building an integrated, secure, and efficient software supply chain.

Where We Solve These Problems, Together

If engineering and security leaders are tired of the massive, impersonal tech carnivals, swampUP is the antidote. We keep the event small, focused, and rooted in authenticity. We are returning to Napa to create an environment for real conversations with industry peers.

People often ask what our CEO, Shlomi Ben Haim, would say is the true purpose of this conference. He’d tell you it’s his favorite day of the year because its mission is simple: pain solving. That’s not a buzzword. It’s a promise. It means we’re not here to sell something; we’re here to articulate the pain a team is feeling right now and give them a direct, practical answer. Attendees won’t see slideware about what might be cool in sixteen years. If a team is struggling with a problem today, we want to fix it. This philosophy is about solving real problems, making solutions practical, and staying grounded in the reality of everyday business.

That’s why the event starts with a full day of deep, hands-on training where attendees will build solutions, not just watch presentations. It’s why our keynotes feature new products they can use immediately. It’s about making a real difference in the work lives of software professionals.

The Quantum Shift is the biggest challenge and the greatest opportunity our industry has ever faced. The shift is happening with or without you.

Join us at swampUP 2025, and let’s lead it together.

swampUP 2025 takes place September 8 to 10 at the Meritage Resort and Spa in Napa, California. Registration is open at swampup.jfrog.com.

The Techstrong team will be on site speaking with industry leaders and attendees to bring you on-the-ground perspectives from the event.

August 26, 2025
Can AI Replace DevOps Engineers?

Let’s cut to the chase: no, it can’t.
Not now. Not anytime soon.

In my experience working with engineering teams across industries, I’ve seen just how fast cloud infrastructure is evolving. Automation and AI are certainly becoming more embedded in daily workflows. And yes, they can take over certain tasks, especially the tedious, repetitive ones. But if you think that means engineers are becoming obsolete, you’re missing the plot entirely.

If anything, what AI is doing is revealing how critical skilled engineers still are, and will continue to be.

The Story Behind the Hype

The industry loves a good headline. “AI is coming for your job!” “DevOps without the devs!” It’s easy to get swept up in that energy.

But the real world isn’t a press release.

Here’s a quick example from a client I worked with recently: they proudly demoed a Terraform configuration to spin up their environment. On paper, it looked solid—clean, properly formatted, no syntax issues. But it didn’t work. At all.

Turns out, the code was AI-generated. It looked polished, but it wasn’t functional. It hadn’t been validated. It didn’t reflect the actual infrastructure setup. It was all surface, no substance – a reminder that code generation without understanding is just theater.

And that’s not a one-off. I’ve seen it happen more than once.

Fast Isn’t Always Smart

Let’s be clear: tools like Copilot, ChatGPT, or Claude can be incredibly helpful. They can speed up scaffolding, suggest fixes, and help with repetitive scripting. But their knowledge comes from public data. They don’t know your stack. They don’t know your security policies. They don’t follow your team’s naming conventions or compliance rules.

In fact, I’ve heard stories of AI-generated infrastructure code that includes deprecated syntax or outdated APIs. One engineer told me he had an entire Azure policy fail silently because the AI-generated config used the wrong version schema. It took hours to debug.

That’s the risk. AI gets you there faster, but if it’s the wrong direction, you just crash sooner.

Augmentation > Automation

Where AI is making progress is in deterministic workflows — tools that operate on rules, not guesses. Think of systems that understand Policy-as-Code and can generate merge-ready fixes based on defined organizational standards.

They aren’t trying to be creative. They’re trying to be right.

This kind of AI behaves more like a very efficient junior engineer: it flags issues, prepares clean pull requests, and works quietly in the background. But it doesn’t deploy without you. It still relies on human judgment and review.

And that’s the point. It’s not replacing anyone. It’s removing friction. It’s streamlining the work so engineers can focus on harder, higher-value challenges.

Even Gartner seems to agree. In their 2024 Hype Cycle for Site Reliability Engineering, they flagged “AI Assistants for Infrastructure as Code” as a rising category, specifically because they enhance, not replace, engineering roles.

Some Things Only People Do Well

No AI tool is going to understand why your U.S. West region is under a deployment freeze due to legal concerns. Or that your team is shipping a critical feature by Friday and can’t risk a surprise regression. Or that your lead engineer is OOO and won’t be reviewing that PR until Monday.

It won’t sit through a sprint retro. It won’t handle an outage call at 2 a.m. It won’t balance trade-offs or debate system design with the team.

Even as AI gets better at writing and fixing code, it still doesn’t handle the nuance. DevOps isn’t just scripts and pipelines. It’s conversations. Decisions. Context.

And none of that’s going away.

Looking Ahead: What Might Change

Now, while I don’t believe engineers are being replaced, I do think their role is shifting. We’re seeing the rise of what I’d call “AI-aware engineers.” These are people who guide AI tooling — tuning it, checking its output, defining the rules it should follow.

Expect to see more emphasis on policy-as-code, declarative security, and infrastructure governance baked into CI/CD pipelines.

A CNCF survey recently reported that 67% of platform teams view AI-enabled automation as a top investment. Not to cut headcount but to eliminate manual toil.

This is the new path: let AI handle the routine, while humans tackle the real problems.

Final Thoughts

Trying to replace DevOps engineers with AI misses the point entirely. Automation is valuable, sure. But DevOps is fundamentally about context, coordination, and accountability – things no machine is ready to own.

What we should focus on is how to best pair engineers with the right tools. Let AI take the grunt work. Let people steer the ship.

Because the future isn’t hands-free. It’s hands-on – just with a lot more support behind the scenes

August 26, 2025
Redefining Engineering Excellence: How Product Skills Amplify Your Impact in the Era of AI
The rapid evolution of artificial intelligence (AI) is transforming the landscape of multiple industries, including software development. The question isn’t whether AI will change the role of software engineers — it already has. The real question is: How can engineers amplify their impact and thrive in this new reality?

As a professional with over five years of experience in software engineering, including roles such as tech lead, engineering manager and platform engineering lead at one of the world’s largest job aggregators, I have seen firsthand how much engineers dislike repetitive and routine tasks. Today, AI allows engineers to delegate these tasks to a ‘personal junior engineer’ that everyone has always dreamed about. This raises one of the many important questions: What is next for software engineers?

This is, indeed, an interesting question. To address it effectively, I propose we apply first principles thinking — a method that involves deconstructing complex problems into their most basic elements. One practical exercise within this framework is the ‘Five Whys’ technique, which can help us understand how AI can enhance software engineers’ roles rather than replace them.

Let us break it down:
- Why do engineers write code?
To build new features
- Why do engineers need to build new features?
To solve problems or meet user needs
- Why do we need to solve problems or meet user’s needs?
To create value for the business and improve user experience (UX)
- Why do we need to create value for the business?
To ensure the company’s growth and sustainability
- Why is the company’s growth and sustainability important?
It provides jobs, drives innovation and contributes to the economy
- Why does this matter at all?
As Steve Jobs said, “Innovation distinguishes between a leader and a follower.” Innovation isn’t just about solving today’s problems; it is about imagining what doesn’t yet exist and having the courage to create it. The sixth ‘why’ reminds us that we innovate not just for utility or profit but to leave a mark — to contribute something meaningful to the world.

By drilling down to these fundamentals, we see that software engineering is about creating value and innovation, allowing engineers to impact not just the ‘how’ of software development, but also the ‘what’ and ‘why’. This strategic involvement requires human insight and creativity — areas where AI cannot replace us.

The Shift From Software Engineers to Product Engineers

Traditionally, software engineers have focused on delivering clean, efficient and scalable code. While technical expertise remains critical, the rise of AI is shifting the emphasis from execution to strategy and problem-solving. Developers who incorporate product thinking into their work will position themselves as indispensable assets to their organizations.

A product engineer is more than just a code expert. They bridge the gap between development and business impact by:
- Understanding product strategy and objectives and key results (OKRs)
- Understanding user needs and designing solutions that enhance UX
- Identifying opportunities to contribute to product strategy
- Developing a product mindset
Why AI Makes Product Thinking Essential for Engineers

AI is not just a tool for automating repetitive coding tasks — it is also reshaping how products are built, tested and deployed. As AI models generate code, optimize infrastructure and even predict software failures, engineers must redefine their role in the development process.

To remain valuable, developers must shift their focus from ‘How should I build?’ to ‘What should I build and why?’ By embracing product skills, engineers can:
- Become ‘Thinkers’, Not ‘Doers’: AI can generate code, but it cannot deeply understand user pain points, business objectives and long-term scalability. Product engineers connect the dots between technology and business value.
- Lead Cross-Functional Innovation: AI-powered automation frees up engineers to drive innovation rather than get stuck in execution mode. Product-focused engineers work at the intersection of technology, business and UX. They lead strategic initiatives rather than just implement them.
- Design With AI in Mind: The best engineers do not just use AI tools — they design for them. Understanding how AI impacts user workflows, data privacy and ethical considerations gives engineers a competitive advantage in building future-ready products.
Key Product Skills Every Engineer Should Develop

For engineers looking to stay ahead in an AI-driven landscape, developing product skills is no longer optional — it is essential. Here are the key areas to focus on:

Understanding Product Strategy and OKRs

Regardless of the role you hold in a company, you are hired to deliver value. Your salary is an investment and the company expects a return. Each organization defines value differently, but ultimately, it is linked to revenue — a clear indicator that users love the product and are willing to pay for it. They exchange money for the value they receive.

How can engineers focus on delivering this value? The answer lies in understanding product strategy. Product strategy isn’t just about deciding what to do; it is also about knowing where not to waste time and energy.

To gain this understanding, engage with the product team and be curious about OKRs and product strategy. Understand the company’s goals for the next year or five years and learn how the company plans to achieve them.

For example, imagine your company sets an OKR to increase e-commerce sales by 15%. The product strategy involves enhancing the mobile app’s checkout process to reduce abandonment rates. By understanding these business objectives, you now have guidance on how to deliver value. You can generate ideas to achieve the OKR, aligning your technical skills with business goals and contributing directly to the company’s success.

User-Centric Thinking

Now that we are clear on the goal, let us revisit the second ‘why’ in our exercise: Why do engineers need to build new features? The answer is simple — to solve problems or meet user needs. How can we solve user problems if we do not truly understand who our users are and what their problems might be?

This brings us to the second important skill: Having a strong understanding of user needs. There are many frameworks available to help structure this understanding. Two that I find particularly useful are Jobs To Be Done and Customer Journey Mapping.

Now, let us return to our example of the checkout process. We know the abandonment rate is high, but why? To answer this, I suggest approaching the problem from two angles:
- Qualitative Data: Ask questions such as, ‘Do we have user insights about the current pain points in the checkout flow?’
- Quantitative Data: Analyze metrics to understand, ‘Where are users experiencing friction in the checkout flow?’
For instance, imagine users report that the payment screen is loading too slowly. By comparing this feedback with quantitative data (e.g., drop-off rates), you can validate their concerns and identify actionable solutions.

Developing Product Solutions

As you dive deeper into product thinking, you will start to identify potential solutions to business challenges. For instance, you might hypothesize that optimizing page load times could significantly reduce checkout abandonment rates.

However, it is crucial to remember that this is just a hypothesis, not a proven solution. This is where your product management skills come into play. Instead of jumping straight to implementation, approach it strategically:
- Frame your idea as a testable hypothesis
- Use frameworks such as data, insight, belief, bet (DIBB) to structure your proposal
- Outline a plan to validate your hypothesis through experiments or A/B testing
By presenting your ideas in a structured way, you demonstrate both technical expertise and strategic thinking that align with business goals.

Product Mindset

I have learned that developing a product mindset starts with curiosity and a critical, nonjudgmental approach. I always ask myself: Is what I am building really valuable? How valuable is it? I try to think like an investor — would I put my own money into this? What would I get in return?

Stay curious, I say. Question everything. Just because we have always done something one way does not mean we should keep doing it. I love digging into data to see how customers actually use our product. Trust me, it can surprise you.

For me, it is about developing a real passion for the problem. I am fascinated by payments, always looking for ways to make things simpler for merchants. Payments are complex, but our job is to make them feel simple.

I have learned to be proactive. I treat the company like it is my own. I do not wait for features to work on — I know the company’s goals. I figure out how to hit those goals, come up with ideas and pitch them.

The Future of Engineering

Engineers are in a unique position to lead. My advice? Take initiative. Get buy-in. Drive the product forward. Think differently. That is how you make a real difference.
July 31, 2025