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Managing Day 2 Concerns for Agentic AI Architecture
We’re now in the fifth wave of computing. If that sounds dramatic, that’s because it is—and probably also because someone in marketing really wants to make “agentic AI” stick. For those keeping track at home, the first four waves were mainframes (your granddad’s computer), web (when we were all hopeful), cloud (when we were still kind of hopeful), and mobile (where that hope became a subscription to your attention span).

And now? Agentic AI.

Let’s get the buzzwords out of the way: Agentic AI is a fancy term for giving large language models (LLMs) persistent memory, looping logic, and callbacks to reach out to your APIs. It’s like giving a Roomba a graduate degree and asking it to run your IT department. These “agents” operate independently (ish), have goals (ish), and make decisions (ish) based on prompts, semantic search and however much context you can cram into a vector database.

Sounds fantastic. Until you have to operate and maintain this stuff in production.

Because Day 1 is always a party, demos run great on slide decks. Day 2 is when the ops team walks into the room, sees the cloud bill, and quietly walks back out.

The Scale Problem (a.k.a. We Broke TPS)

Let’s talk about scale. Traditional systems had thousands to millions of users. With mobile, we got billions. Agentic AI? Try trillions. Not just people, though. You’ll have agents helping users, agents helping agents and agents judging the work done by other agents.

Mainframe

Web

Cloud

Mobile

Agentic

Users

thousands

millions

100 million

billions

trillions

TPS

100

500

2,500

10,000

1,000,000

5x

5x

4x

100x

Each one of those agents runs loops. Each loop makes calls. Each call hits an LLM. Every LLM call contributes to Transactions Per Second (TPS). Not all transactions or LLM interactions are the same. Humans have very different behavior than agents.

The types of interaction behavior we might see:
- Human users = finite and sleepy
- Agents = infinite and caffeinated
- LLM inference = expensive, slow, and shockingly unreliable
- Therefore: system load = Who knows? How do we even predict this stuff?
It isn’t unusual for pre-agentic systems to max out around 10,000 TPS. Agentic systems? You’re looking at a million TPS just to hit parity with your company’s Slack usage.

But What About Cost?

Ah yes, the CFO’s favorite section.

Running agents is expensive. Running smart agents is very expensive. Running smart agents 24/7 with high-accuracy LLMs? Welcome to a world where your annual AI ops bill might be an order of magnitude higher than it was before.

Let’s put that in relatable terms:
- GPT-4 32K (86% accuracy): $98 per million tokens
- Open source model (42% accuracy): $0.013 per million tokens
- Database transaction: pocket change by comparison
And don’t let the open-source optimism fool you. A 42% accurate agent might write emails like “Dear Sir/Madman, I have eaten the spreadsheet,” which is cute until it’s running your treasury. Remember that hallucinations and bad responses are so common, we already have patterns for using multiple other LLMs just to judge the work of the first. This means that your cost isn’t just the cost to ask an LLM an innocent question, it’s the cost to resolve tools, cost to make the actual request and cost to make potentially multiple judgment requests.

So now you’re faced with the AI version of the eternal tradeoff: dumb and cheap vs. smart and broke.

Latency, Reliability and Other Sad Stories

LLMs are not built for low-latency, high-throughput work. They’re built for… storytelling. And hallucinating. And sometimes – if you ask nicely and are an expert in prompt engineering – even answering your questions.

Now, imagine your core systems relying on that. Imagine your uptime metrics depending on the whims of an LLM inference API that thinks “return customer data” means “find a retail location where customers can bring and return their data”

Agentic systems aren’t transactional. They’re conversational. They’re unpredictable. They can fail to give you an answer, they can stop answering in the middle of a stream. Don’t listen to what people tell you, you can’t blindly trust an LLM.

And yet, we keep trying to run them like traditional software. We build orchestration platforms, routing layers, retry loops, and cost governors. Why? Because otherwise, these things will try to eat the entire internet in a single for-loop and send us the cloud bill.

Capacity Planning Is Now Chaos Planning

You used to think about load in terms of peak usage. Now? Your agents don’t sleep. They don’t go to lunch. They just keep going, looping away, slamming vector DBs and semantic search engines with relentless, existential enthusiasm. Your system lows are higher, your median times are higher, and your peaks are higher.

This means:
- Your database load goes up 100x
- Your network saturates under constant agent chatter
- Your infrastructure team turns into a stress-testing experiment
- And you still get worse answers than just asking the intern
You don’t plan for “peak load” anymore. You plan for “non-stop Reddit debates between bots.”

The Blueprint (Such as It Is)

In theory, building a sustainable agentic architecture looks like this:
1. Semantic Search – All your domain knowledge gets shoved into a vector DB, ready to be retrieved by semantic similarity.
2. Agentic Sessions – Every agent interaction is logged to provide context, continuity and accountability.
3. LLM Invocation Loops – Prompt, retrieve, augment, call LLM, process, repeat until you get a satisfactory answer, or you run out of usage quota.
4. Orchestration Layer – The puppet master that tells agents what to do, when, and for how long before they bankrupt you.
This is all manageable, assuming you can hire a team of distributed systems engineers who also speak fluent “prompt engineering,” understand vector math, and have no plans for sleep.

What Ops Teams Need to Do (Besides Cry)

If you’re serious about building something in this space—and not just gluing ChatGPT to your CI pipeline, you need to:
- Plan for real-time TPS: Millions of them, all at once, with unpredictable spikes.
- Plan for augmentation (RAG): Simple one-off queries are rare. Instead, your agents will be making multiple round trips to the LLM to resolve tools and multiple passes at a vector database for context augmentation.
- Instrument your agents: Know which model they’re using, how often, and at what cost.
- Design for failure: Assume models will hallucinate, APIs will fail and your agents will suddenly think they’re writing sci-fi.
- Stay modular: You’ll want to swap models faster than your CFO can say “cost optimization.”
Final Thought: The AI Agency Gap

The AI Agency Gap is the chasm between dumb bots and actual autonomous systems. It’s the space where most agents will live—semi-capable, semi-stable, semi-helpful. They’re the interns of the software world: energetic, inconsistent and always one bad prompt away from disaster.

If you want to thrive in this new wave of computing, you’re going to need resilience, observability, guard rails, patience and some really good stress coping techniques. The future isn’t just about building agentic systems, it’s about knowing that you’ve made all of your microservices unpredictable and require 10x more resources.

Good luck.
June 5, 2025
Logz.io Unveils Observability Platform Designed First for AI Agents

Logz.io today launched an edition of its observability platform that is intended to be primarily used by artificial intelligence (AI) agents rather than DevOps engineers.

Company CEO Tomer Levy said that as DevOps continues to evolve it’s become apparent that most tasks pertaining to observability will soon be assigned to AI agents. This edition of the Open 360 observability platform exposes machine-readable schemas alongside every panel, allowing AI agents to autonomously create charts, interpret anomalies and modify entire dashboards.

Rather than simply adding a chat bot interface to an existing observability platform, Logz.io is moving to make it possible for DevOps engineers and AI agents to collaboratively observe complex IT environments using a common set of dashboards, said Levy.

That approach still produces insights via a dashboard that can still be read by humans, but the assumption is that AI agents will be the primary end user of those dashboards, he added.

Tasks assigned to those AI agents include executing custom playbooks created by DevOps teams, reviews of new application deployments, cost analysis and security assessments. AI agents can also automatically generate visualizations based on natural language requests, interpret and analyze anomalies in real time and interact with other agents to automate more complex tasks.

Logz.io has already tested this AI agent-first approach with more than 200 IT organizations that have deployed more than 2,000 Logz.io AI Agents, resulting in saving a total of approximately 25,000 engineering hours this month alone—equivalent to 12 full-time SRE positions. At the current adoption rate, Logz.io projects customers will recover roughly 300,000 hours of engineering time over the next year.

It’s not clear how extensively AI agents will change the way DevOps workflows are managed, but it is apparent that with the rise of AI coding tools, the number of applications being deployed by any organization is about to significantly increase. Most existing DevOps teams are not going to be able to keep pace with that rate of application development and deployment without being augmented in some way by AI agents.

The challenge then becomes determining which tasks are best assigned to an AI agent versus those that still require the expertise of a DevOps engineer. Over time, however, DevOps engineers will find themselves supervising a small army of AI agents that have been trained to perform a range of specific tasks.

Ultimately, AI agents should make applications both more resilient and secure as anomalous behavior is identified sooner. Armed with those insights, it then becomes feasible for application developers that are similarly augmented by AI agents to troubleshoot applications in a way that results in more issues being resolved long before they have any meaningful impact on the organization.

In fact, AI agents should make it possible for more organizations to benefit from observability by proactively addressing application issues rather than simply continuing to monitor a set of pre-defined metrics that provide limited actionable intelligence. That, of course, might take some time for IT professionals to accept and fully trust, but in time, many of the tasks that conspire to make managing DevOps workflows more tedious will, like it or not, be increasingly performed by AI agents.

June 5, 2025
Postman Adds AI Agent Mode to Design, Build and Deploy APIs

Postman today revealed it has developed artificial intelligence (AI) agents for its platform for designing and building application programming interfaces (APIs).

Announced at the POST/CON 2025 event, the Agent Mode enables developers to, via a natural language interface, automate entire multi-step workflows spanning the designing, testing, documenting and monitoring of a single API or a logical group of APIs, known as collections.

Postman CEO Abhinav Asthana said unlike an AI copilot, Agent Mode natively executes tasks in a way that understands the actual intent of an API developer.

The overall goal is to reduce the amount of time required to build and deploy an API from weeks to a few hours using a set of repeatable workflows created by an AI agent, he added.

Additionally, developers will be able to integrate the Postman agent with other AI agents via a model context protocol (MCP) server that Postman added last month.

Finally, Postman is also committing to make it possible for developers to alternatively employ AI agents they create to build, develop and test APIs. Postman Insights, now available in a more open beta, will then provide the tools required to track usage of any API created in real time, noted Asthana.

It’s still early days so far as usage of AI tools to build APIs is concerned, but reliance on them within AI applications is already pervasive. Based on data collected from the Postman API Platform between May 2023 and April 2025, developers made more than 7 million API requests involving AI. The OpenAI platform accounted for 60% of that activity, but API traffic involving Meta Llama and Google Gemini grew by a factor of seven and five, respectively.

In total, there are more than 40 million developers that make use of Postman tools to build APIs for 500,000 organizations. The Postman API Network is being used to expose more than 100,000 of those APIs to drive multiple application services. As AI agents make it simpler for both developers and now non-technical end users to build and deploy APIs, the volume of activity is only going to continue to exponentially increase, noted Asthana.

Each organization will need to determine how extensively to rely on AI agents to build and deploy APIs, but it will, if necessary, be possible to rollback any tasks completed by an AI agent, said Asthana.

The one thing that is certain is that API documentation should substantially improve. Most application developers don’t especially enjoy creating documentation, which can be a significant issue whenever a DevOps team needs to troubleshoot an application.

Hopefully, an AI agent trained to develop APIs will lead to higher quality. APIs, in particular, have become the focal point for cybersecurity attacks because in addition to enabling the exfiltration of data they can also be a vehicle for corrupting business logic.

In the meantime, however, IT leaders in the months ahead might want to revisit not just how APIs are created, but also managed, secured and updated in the age of AI.

June 4, 2025
DevOps Tools Under Siege: New Cryptojacking Campaign Exploits Misconfigurations to Mine Cryptocurrency

A sophisticated cryptojacking campaign is targeting the heart of modern development infrastructure, exploiting misconfigurations in popular DevOps tools to deploy cryptocurrency mining operations at scale. Security researchers at Wiz have uncovered what they’re calling a widespread attack that marks a concerning evolution in how threat actors target development environments.

The campaign, attributed to a threat actor dubbed JINX-0132, represents a calculated shift toward exploiting the very tools that organizations rely on to build, deploy and manage applications. Unlike traditional attacks that focus on end-user systems or generic servers, this operation specifically targets HashiCorp Nomad, Gitea, HashiCorp Consul and Docker API servers. These technologies sit at the core of DevOps workflows.

A New Frontier in Cryptojacking

What makes this campaign particularly noteworthy is its approach to stealth and persistence. Rather than deploying custom malware that security tools might flag, JINX-0132 deliberately avoids unique identifiers that could serve as indicators of compromise. Instead, the threat actor downloads tools directly from public GitHub repositories and uses standard release versions of XMRig, a legitimate mining software, making detection significantly more challenging.

This strategy demonstrates a sophisticated understanding of how modern security tools operate. By blending in with legitimate traffic and using trusted sources for their tools, the attackers can operate under the radar while maximizing their cryptocurrency mining operations across compromised infrastructure.

The Nomad Breakthrough

Perhaps the most significant discovery in this campaign is the first documented exploitation of HashiCorp Nomad as an attack vector. Nomad, a workload orchestrator that helps organizations deploy and manage applications across their infrastructure, has become an attractive target due to its powerful job scheduling capabilities.

The attackers exploit Nomad’s job queue feature, which isn’t secure by default in many deployments. By abusing this functionality, they can create and execute multiple malicious jobs that deploy their mining software across the victim’s infrastructure. This approach is particularly effective because it leverages the platform’s native capabilities, making the malicious activity appear as a legitimate workload management process.

The implications extend beyond just cryptocurrency mining. Once attackers gain access to Nomad’s job scheduling system, they can potentially deploy any workload across an organization’s infrastructure, opening the door for data theft, lateral movement and other malicious activities.

Consul and Docker: Familiar Targets, New Techniques

While Nomad represents new territory, the campaign also targets more established attack vectors with refined techniques. HashiCorp Consul, designed to secure network connectivity between services across on-premises and multi-cloud environments, is being exploited through its health check service functionality.

The attackers hijack Consul’s health check mechanism to execute bash commands and download XMRig payloads. This technique is particularly effective because health checks are a regular part of Consul’s operation, making malicious activity harder to distinguish from legitimate monitoring traffic. Unless organizations have properly configured access control lists or enabled HashiCorp’s security features, any user with remote access can register services and health checks, providing an easy pathway for remote code execution.

The campaign also exploits CVE-2020-14144 in older versions of Gitea, an open-source alternative to GitHub, as well as misconfigured Docker Engine API servers. In Docker environments, the attackers create containers that launch cryptocurrency miner images, effectively turning the victim’s container infrastructure into a distributed mining operation.

The Scale of Risk

The research reveals alarming statistics about the potential attack surface. According to Wiz data, 25% of all cloud environments run at least one of the targeted technologies, with HashiCorp Consul being the most prevalent, running in over 20% of environments. Of those environments using these tools, 5% expose them directly to the internet, and among those exposed deployments, 30% are misconfigured.

These numbers translate to a significant attack surface that threat actors can exploit. The widespread adoption of these DevOps tools, combined with the frequency of misconfigurations, creates an environment where cryptojacking campaigns can operate at scale with minimal risk of detection.

Defense Strategies for DevOps Teams

The good news is that organizations can take concrete steps to protect themselves from these attacks. The key lies in proper configuration and implementing security best practices that are often overlooked in the rush to deploy new infrastructure.

For Nomad deployments, teams should implement access control lists and enable the security features outlined in HashiCorp’s Security Model documentation. This includes configuring proper authentication and authorization mechanisms to prevent unauthorized job submissions.

Consul users should activate the security features detailed in HashiCorp’s Secure Consul documentation, including disabling script checks and restricting the HTTP API to bind only to localhost where possible. These configurations prevent attackers from exploiting the health check functionality for remote code execution.

Gitea administrators must keep their instances up to date to prevent exploitation of known vulnerabilities, such as CVE-2020-14144. Additionally, organizations should avoid enabling git hooks or leaving installations unlocked unless necessary, as these features can provide additional attack vectors.

For Docker environments, proper API configuration is crucial. Organizations should ensure that Docker APIs are not exposed to the internet without appropriate authentication and implement container security best practices to prevent unauthorized container creation and execution.

The Broader Impact on DevOps Security

This campaign highlights a critical shift in the threat landscape that DevOps teams face. As these tools become more central to how organizations build and deploy software, they inevitably become more attractive targets for threat actors. The sophistication demonstrated by JINX-0132 suggests that other groups will likely adopt similar techniques, making DevOps security an increasingly critical concern.

The attack also underscores the importance of security-by-default configurations in DevOps tools. Many of the vulnerabilities exploited in this campaign stem from insecure default settings that prioritize ease of deployment over security. As the DevOps ecosystem continues to evolve, both tool developers and organizations need to prioritize security configurations that protect against these types of attacks without compromising usability.

“The JINX-0132 campaign exploiting DevOps tools is a critical wake-up call; the agility DevOps delivers cannot overshadow foundational security for the tools managing our software pipeline,” according to Mitch Ashley, VP and practice lead, software lifecycle engineering at The Futurum Group. “Attackers leveraging misconfigured orchestrators like Nomad and hiding in plain sight with legitimate mining tools signify a sophisticated threat evolution targeting the core of modern development.”

Looking Forward

The JINX-0132 campaign serves as a wake-up call for the DevOps community. As development workflows become increasingly automated and distributed, the security of the tools that enable these workflows becomes paramount. Organizations that fail to properly secure their DevOps infrastructure risk not only cryptocurrency mining attacks, but potentially more damaging intrusions that could compromise sensitive data or disrupt critical services.

The key to defense lies in treating DevOps security with the same rigor as traditional IT security. This involves implementing proper access controls, keeping tools up to date, adhering to security best practices, and continuously monitoring for signs of compromise. By taking these steps, organizations can continue to benefit from the efficiency and flexibility of modern DevOps tools while protecting themselves from emerging threats.

As threat actors continue to evolve their techniques, the DevOps community must evolve its defenses. The discovery of the JINX-0132 campaign provides valuable insights into these emerging threats and offers a roadmap for building more secure development environments. The question isn’t whether similar attacks will continue to appear, but whether organizations will be prepared to defend against them.

June 3, 2025
How to Elevate DevOps Training and Testing With Generative AI
Everyone is talking about artificial intelligence (AI). More specifically, they are buzzing about generative artificial intelligence (GenAI), the chatbots with the perceived potential to usher in a universal utopia or destroy the world (depending on who you talk to).

These extremes are speculative at best, foretelling an uncertain future shaped by GenAI.

In the meantime, GenAI tools, like ChatGPT, Claude, Gemini and the countless other models popping up daily, are good coders.

In fact, they are really good coders.

More specifically, it is a good coding assistant. One study found that GenAI increases coders’ productivity and capabilities, expanding their aptitude for new data science tasks, even without prior experience in coding or statistics.

However, in 2025, we don’t just need more software. We need safer software that is resilient against even the most pernicious cybersecurity threats, putting companies and customers at risk.

For small startups and major corporations, this places increasing importance on DevOps and software development skills, training and certification.

This is where GenAI can really help companies. In addition to producing new code, it can enable companies to teach their tech teams how to protect sensitive data, maintain regulatory compliance and mitigate emerging security risks at scale.

Training and Testing Development is Difficult

DevOps teams need cutting-edge training and testing to prepare them for increasingly sophisticated and constantly emerging cybersecurity threats.

Creating training and testing programs that provide actionable information and don’t just check boxes and waste time is time-consuming, labor-intensive and expensive.

To produce truly effective training programs, in-house or third-party certification programs rely on a rigorous and systematic process involving iterative processes with subject matter experts (SMEs) and other experts who use their experience and expertise to guide content development.

Test creators leverage their insights to generate training material and test items, which are subsequently reviewed by a panel of SMEs, who modify these draft questions for accuracy, appropriateness, difficulty and relevance.

It’s a workflow ripe for new efficiencies that make creating high-quality training and testing materials more agile and responsive while controlling costs and improving consistency.

This is where GenAI can help companies streamline and enhance this complex workflow.

GenAI in DevOps Testing and Certification

GenAI offers compelling DevOps training and certification opportunities but also presents unique challenges.

For example, the technology’s ability to understand and generate natural language allows for the creation of realistic and engaging test scenarios. At the same time, GenAI can enhance and simplify spell-checking and grammar correction, ensuring the accuracy and professionalism of test items while making it more feasible to continuously create and improve training and testing content as the DevOps and cybersecurity field continues to change.

Taken together, GenAI offers a cost-effective solution for test development, potentially reducing the resources required for manual test creation.

To be sure, these are productivity and cost-control improvements, not full-scale automation because the technology still poses unique risks and challenges for certification programs. This includes:
- Accuracy and Reliability: GenAI platforms still make mistakes, hallucinating and confidently presenting wrong information as right.
- Bias: GenAI models can inherit biases from their training data, leading to unfair or discriminatory test content.
- Security and Privacy: Using commercial GenAI products can make it difficult to protect the security and privacy of sensitive test and test-taker data.
- Rapid Evolution: GenAI technology is both novel and rapidly changing, making it difficult for companies to keep up with the rate of change and the implications for their work.
- Legal Uncertainty: There is still uncertainty about the copyright status of AI-generated content.
Perhaps most prominently, because GenAI technology is still so new, its greatest strength and most significant shortcomings are probably still being discovered.

In response, assessment creators must stay adaptable and proactive, modifying their strategies to capitalize on GenAI’s advantages and minimize its drawbacks as the technology evolves.

Best Practices for Implementing GenAI in DevOps Testing and Certification

To harness the full potential of GenAI while mitigating its risks, several best practices can guide organizations in creating effective and responsible AI-powered testing and certification programs.

#1 Treat GenAI as an Assistant

Use AI to augment human expertise, not replace it. Subject matter experts should work alongside GenAI tools to ensure the quality and relevance of test content.

For example, GenAI can create practice tests or question variants that benefit the overall testing experience but don’t undermine expertise or production quality.

#2 Focus on Quality Control

Inaccurate training is the same as no training at all. When using GenAI tools to support training and test creation, robust quality checks ensure the accuracy, fairness and relevance of AI-generated content.

That’s why human review and validation are essential to integrating GenAI into the content creation process.

#3 Prioritize Data Security

Protect the privacy and security of test-taker data and sensitive test content. Use secure platforms and tools that comply with relevant data privacy regulations.

Put differently, don’t just put your test information into ChatGPT. Instead, leverage third-party tools that protect privacy and prevent your data from entering the public domain.

#4 Get Good at Prompt Engineering

Effective prompt engineering, which involves designing and refining input prompts to optimize GenAI model performance, is necessary for creating high-quality exam assets.

#5 Commit to Continuous Improvement

Continuously evaluate and refine the use of GenAI in testing and certification processes. Monitor the effectiveness of AI-powered tools and adapt strategies as needed.

Enhancing Security and Productivity With GenAI

Undoubtedly, GenAI can help organizations develop and validate the skills needed to build and maintain secure and reliable software.

Even so, training and test creators will need to integrate the technology thoughtfully and intentionally, addressing potential challenges and prioritizing data security and quality control.

Organizations can leverage GenAI to enhance their DevOps training and certification programs by embracing these best practices and remaining adaptable.

The result? Meaningful progress toward a more secure and efficient software development lifecycle.
June 3, 2025
Five Great DevOps Job Opportunities

DevOps.com 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

Johnson & Johnson
Santa Clara, CA
Senior DevOps Engineer
$172,931 to $180,000

Indeed.com

EasyPost
Remote
Senior DevOps Engineer
$153,000 to $195,000

Dice.com

General Dynamics
Pittsburgh, PA
Advanced Cloud DevOps and Platform Engineer
$139,189 to $145,000

The Job Network

Sharetec
Fort Wayne, IN
DevOps Manager
$130,000 to $140,000

LinkedIn

Meritore Technologies
Scottsdale, AZ
DevOps Engineer
$125,000 to $135,000

June 2, 2025
Redgate Extends Effort to Integrate Database Management and DevOps Workflows

Redgate has updated its Flyway platform for applying DevOps workflows to database platforms to now provide deeper integration into command line interface (CLI) tools and integrated developer environments (IDEs).

In addition, the Flyway platform now enables state-based deployments using scripts that can be generated based on a specific schema.

Finally, Redgate is also adding options that enable DevOps teams to choose whether they want to include backups as baselines, automate the backup process altogether or never allow any type of shadow database.

Max Drobot, group product manager at Redgate, said these latest additions are intended to make it simpler for software engineering teams with varying levels of maturity to start automating the management of databases within the context of a DevOps workflow. Rather than trying to create the perfect process, it’s now simpler for DevOps teams to more easily experiment with automating database deployments without increasing risks, he added.

While DevOps engineers and database administrators (DBA) often need to work closely with one another, each of these teams has historically developed a separate culture around different classes of tools. Since it first launched Flyway, Redgate has been making a case for a platform that helps narrow that divide by enabling DevOps engineers to, for example, spin up a database when needed versus waiting for a DBA to have the time that would otherwise be required.

That approach doesn’t eliminate the need for a DBA, but it does serve to reduce the overall level of friction that often exists between DevOps teams and DBAs, noted Drobot.

The need to reduce that friction will become that much more pressing as the amount of data that needs to be incorporated into the next generation of artificial intelligence (AI) applications continues to exponentially increase, he added.

Additionally, DevOps teams as part of building and deploying more secure applications will also eventually need to programmatically apply policies and guardrails to the data being accessed by their applications, noted Drobot.

Managing databases has, of course, become more challenging as the number and types of them has steadily increased. Provisioning and managing databases using legacy manual processes is simply no longer tenable, he said.

Many organizations are already embracing platform engineering as a methodology for managing DevOps workflows at scale, so it’s now more a question of when database operations will become more centrally managed within the context of a software development lifecycle (SDLC).

In the meantime, DevOps teams might want to review how application developers are selecting databases for building applications today. All too often, rather than adhering to corporate standards, those decisions are based more on personal preference. In fact, it’s not uncommon for IT teams to find themselves spending time migrating an application developed on one database to another that the organization actually supports.

Each organization will, of course, need to decide on its own to converge the management of DevOps and database operations but given the pace at which modern applications are now being developed and updated in the age of AI, the amount of pressure being applied to achieve that goal is only going to continue to increase.

May 30, 2025
How DevOps Powers Boundless Automation in Industrial Environments
In an era of digital transformation, automation is no longer a luxury — it’s a necessity. Industrial environments, once resistant to rapid technological change, are now embracing automation to streamline operations, reduce errors and enhance productivity. At the heart of this shift lies DevOps: A methodology that fosters collaboration, accelerates deployment and enables continuous improvement.

DevOps methodologies are driving boundless automation in traditionally complex operational technology (OT) settings. From manufacturing to utilities and logistics, DevOps professionals are uniquely positioned to bridge the gap between software and machines, transforming industrial workflows through smarter, more integrated systems.

The Role of DevOps in Industrial Automation

DevOps is built on principles of collaboration, automation, iterative improvement and shared responsibility. While these ideas originated in software development, their impact is now reshaping industrial environments as well.

In complex OT settings where machines, sensors and control systems operate 24/7, manual workflows and siloed teams can lead to inefficiencies and costly downtime. DevOps helps eliminate these roadblocks by applying the same automation-first mindset that revolutionized software delivery. Here’s how DevOps is transforming industrial automation:
- Continuous Integration and Deployment (CI/CD): Automated pipelines allow for rapid testing and deployment of industrial control software, firmware updates and configuration changes without disrupting production.
- Infrastructure as Code (IaC): OT networks, PLC configurations and device settings can now be defined and managed programmatically, ensuring consistency across facilities and eliminating human error.
- Monitoring and Observability: DevOps enables real-time monitoring of both digital systems and physical assets, allowing operations teams to spot trends, detect anomalies and act proactively.
- Feedback Loops: DevOps encourages short feedback cycles — essential for refining industrial automation strategies and minimizing operational waste.
Bulletproofing the Plant Floor With Automation

By integrating DevOps tools and culture into industrial settings, organizations unlock powerful automation benefits that drive productivity and reliability. Consider the following examples of key areas where automation excels:
- Machine Configuration & Provisioning
  Automate the setup and calibration of control systems, reducing deployment time and ensuring standardized environments across multiple sites.
- Data Collection and Analysis
  Collect data from industrial sensors and edge devices, such as handheld gas flow meters and feed it into centralized platforms for predictive maintenance, compliance reporting and optimization.
- Incident Response and Recovery
  Automatically detect and respond to system anomalies, minimizing downtime and preventing small issues from escalating into production halts.
- Safety and Compliance Audits
  Automate routine safety checks, environmental measurements and audit reporting for seamless regulatory compliance.
Case in Point: Empowering Maintenance Teams

Maintenance in industrial environments has historically been reactive. However, when DevOps is combined with smart asset management tools, it evolves into a proactive, data-driven discipline.

For example, teams can use IoT-connected devices like a handheld gas flow meter to gather real-time readings. If the flow levels are abnormal, the system automatically creates a ticket in the maintenance platform. It then cross-references the issue with historical data to determine the likely cause. Based on that analysis, a repair recommendation is generated and scheduled without human intervention. This kind of intelligent automation reduces downtime, improves safety, ensures compliance and extends the life of equipment.

Culture and Collaboration Are Key

Implementing DevOps in industrial settings isn’t just about tools — it’s about fostering a culture that supports transformation. Automation thrives when cross-functional teams work toward shared goals and openly exchange knowledge.

When DevOps professionals collaborate with OT teams, they help build a culture of shared accountability for uptime and performance. Together, they can plan automation initiatives in sprints, hold regular retrospectives to refine processes and continuously improve operations. These cultural shifts can be challenging, but they’re also where the most impactful change happens. Once IT and OT teams begin speaking the same language, innovation accelerates — and so does progress.

Overcoming Industrial Challenges With DevOps Thinking

Applying DevOps to industrial automation is not without its challenges:
- Legacy Systems: Many facilities still run outdated equipment that isn’t easily integrated with modern tools.
- Cybersecurity Concerns: Expanding networked automation increases the attack surface, requiring built-in security practices from the start.
- Skill Gaps: Traditional OT staff may be unfamiliar with DevOps concepts, requiring training and change management.
However, these challenges can be addressed through:
- Containerization and API gateways to wrap legacy systems
- DevSecOps practices that build security into every phase of deployment
- Cross-training programs to upskill operations staff on automation tools
The Future Is Automated and DevOps Is the Key

The industrial world is at a turning point. As demand for efficiency, flexibility and data-driven decision-making rises, automation becomes the path forward. And DevOps is the engine that powers that journey.

By applying DevOps principles — automation, collaboration, continuous delivery and real-time monitoring — practitioners can modernize industrial systems, eliminate silos and unlock powerful gains in uptime, quality and throughput.

For DevOps professionals, the opportunity is clear: Bring your expertise to the plant floor. Whether it’s integrating a handheld gas flow meter into a predictive maintenance workflow or building CI/CD pipelines for PLC software, your skills can help shape the next generation of industrial innovation.
May 30, 2025
Most DevOps Engineer Jobs Pay Upwards of $140K, Require Less In-Ofﬁce Presence and Prefer Senior-Level Experience
The DevOps market is forecasted to reach $25.5 billion by 2028, and the salaries for DevOps roles can go as high as $230,000 per year.

If you would like to explore the DevOps realm or make a switch, it would be beneﬁcial to understand the average pay, preferred experience level, offered working modes and more to make your decision easier.

To analyze all the details for DevOps roles, we will use insights from Kube Careers’ State of the Kubernetes job market report for Q1 2025 — one of the most condensed datasets in the cloud computing domain.

Kubernetes Careers only publishes jobs that require Kubernetes experience, have clear salary ranges and are not published by recruitment agencies. For Q1 2025, they collected over 1,200 jobs, painstakingly narrowing these down to a dataset of 436 for their thorough analysis.

Let’s dig into the numbers:

One in 10 Kubernetes Roles is DevOps

Of all the jobs published on Kube Careers for Q1 2025:
- Software Engineer roles accounted for 47.02% (205)
- Platform Engineer (PE) roles accounted for 11.47% (50)
- DevOps positions followed closely with 9.56% (43).
Additional roles included:
- Site Reliability Engineer (SRE) at 4.82% (21 jobs)
- Machine Learning (ML) Engineer at 4.59% (20 jobs)
- Data Engineer at 3.44% (15 jobs)
- DevSecOps Engineer at 3.21% (14).
In fact, DevOps roles were the second most popular Kubernetes roles in 2024, averaging 9-12% through the quarters.

How Much Does a DevOps Engineer Earn in Q1 2025?

The minimum average salary for DevOps positions is $118,756, and the maximum average is $169,824, bringing the mean salary to $144,290 for Q1 2025.

How Does the Average DevOps Salary Compare to Other Popular Job Titles?

If we compare the average salaries for the most popular job titles in Q1 2025, we ﬁnd that:
- The highest average minimum salary (a better indicator of salary ranges) is for PE roles at $143,001, closely followed by SRE positions at $142,623. Interestingly, DevOps jobs pay the least average minimum salary among the popular job titles (we may just know why, later).
- The hierarchy for the highest average salaries looks like this: PE > SRE > Software Engineer > DevOps Engineer > Machine Learning Engineer. Here, too, DevOps roles are at the bottom two.
- When it comes to the average salary, DevOps has the least at $144,290, which is 20% lower than PE roles and about 5% less than Software Engineer positions.
In fact, the average DevOps salary was $141,645 in 2024, which is now higher by almost 2% in Q1 2025.

The Low Average Salaries for DevOps Positions may be due to the Preferred Experience Level for These Roles

Here are the takeaways when we compare preferred experience levels with popular job titles:
- A certain senior-level experience was required for 85% of the PE roles in Q1 2025, 74.42% for DevOps roles, while 71.71% for Software Engineer roles
- Interestingly, most lead-level jobs were DevOps roles (9.30%); meanwhile, there were none for PE roles
- What stands out is that there were more Junior PE (6.38%) positions than DevOps (2.33%) and Software Engineer (5.37%).
The demand for senior or higher-level experience is similar for most roles, and it doesn’t inﬂuence salaries as much. DevOps salaries are, in fact, lower on average.

Four out of 10 DevOps roles are Remote, Two are Hybrid and Two are In-Ofﬁce

If we look at DevOps positions with respect to working modes, it gets rather interesting.

46.5% of the DevOps roles are remote but they are restricted within a speciﬁc geography (usually within the country), 26.6% are strictly in-ofﬁce roles (five days a week), 23.3% are hybrid, while 4.7% are remote but restricted within a particular time zone (e.g., within -3 to +3 GMT).

Summary

The highlights of this short analysis are:
- The average DevOps salary for Q1 2025 is $144,290.
- Only 20% of DevOps roles require in-ofﬁce presence (5 days a week).
- When it comes to preferred experience levels, 8 out of 10 DevOps positions require senior-level experience.
May 30, 2025
DevOps: More Than a Buzzword—A Mindset of Continuous Evolution
In recent years, the term DevOps has become one of the most talked-about buzzwords in the technology world. You’ll hear it in boardroom discussions, IT conferences, job postings, and even startup meetups. Companies want to “go DevOps,” developers want to work in DevOps environments, and recruiters are constantly looking for DevOps Engineers. But despite all this hype, DevOps is often misunderstood. Many think it’s a tool or a new kind of job title. In reality, DevOps is not a technology—it’s a mindset.

This article explains what DevOps really means, why it’s important, and how it embodies a cycle of continuous practices—like continuous integration, continuous deployment, continuous testing, continuous monitoring, continuous security, continuous improvement, and continuous feedback. We’ll explore these concepts in simple language and explain how they align with business strategy and team collaboration.

What Is DevOps, Really?

DevOps stands for Development and Operations. It brings together software developers (who write the code) and operations teams (who deploy and manage the code in production). Traditionally, these two teams worked separately. Developers would build software and hand it over to operations to release. This handoff model caused delays, confusion, and sometimes even system failures.

DevOps aims to break down these silos. It promotes collaboration, shared responsibility, and faster delivery of high-quality software. DevOps is not a product you can buy or a process you can install. It’s a cultural shift—a new way of thinking about how teams work together to deliver value to customers.

Why DevOps Is a Mindset, Not Just a Tool

Many companies start their DevOps journey by adopting tools like Jenkins, Docker, Kubernetes or Terraform. While these tools help automate tasks, they don’t make an organization “DevOps-ready” by themselves. A DevOps culture is built on principles such as:
- Shared goals between development, operations, and business teams
- End-to-end accountability for software quality and performance
- Fast, safe, and frequent delivery of new features
- Embracing failure as a learning opportunity
- Continuous improvement and feedback
These principles require changes in how people work, how they communicate, and how they measure success. In other words, DevOps is a people-first approach, supported by tools—not the other way around.

Strategic Alignment: Connecting DevOps to Business Goals

One of the biggest benefits of DevOps is its ability to align IT efforts with business goals. When development and operations teams work closely with product managers and business leaders, they can prioritize features that deliver real value to customers.

For example, instead of spending months developing features that may never be used, a DevOps team can release small updates frequently, gather user feedback, and adjust the roadmap. This approach supports agile decision-making, reduces risk, and increases customer satisfaction.

DevOps also emphasizes metrics that matter to the business, such as:
- Deployment frequency
- Lead time for changes
- Mean time to recovery (MTTR)
- Customer feedback and satisfaction
These metrics help teams measure their performance and make data-driven decisions.

The Pillars of DevOps: A Culture of Continuity

DevOps is powered by a series of continuous practices. Let’s walk through each one and understand how they fit together.

1. Continuous Integration (CI)

Continuous Integration is the practice of automatically merging code changes into a shared repository several times a day. Every time a developer writes new code, it is tested and integrated with the existing codebase. This helps catch bugs early and ensures the software always stays in a working state.

Key benefits:
- Faster development cycles
- Reduced integration issues
- Early bug detection
CI tools: Jenkins, GitLab CI, CircleCI, Travis CI

2. Continuous Deployment (CD)

Once the code passes all the tests, it is automatically deployed to production or staging environments. This is known as Continuous Deployment. It removes manual intervention and allows teams to release features to users quickly and reliably.

Key benefits:
- Shorter release cycles
- Faster feedback from users
- Reduced manual errors
CD tools: Spinnaker, Argo CD, AWS CodePipeline

3. Continuous Testing

Continuous Testing ensures that the software is tested at every stage of the development lifecycle. This includes unit tests, integration tests, performance tests, and even security tests. Automated testing tools help identify defects early and prevent bugs from reaching the end user.

Key benefits:
- Improved quality
- Faster bug detection
- Increased confidence in releases
Testing tools: Selenium, JUnit, TestNG, Postman, SonarQube

4. Continuous Monitoring

After the software is released, it’s important to monitor its performance, availability and user behavior in real time. Continuous Monitoring helps detect issues early, prevent downtime, and understand how users interact with the application.

Monitoring also provides telemetry data, which can be used to improve future versions of the product.

Key benefits:
- Better uptime
- Real-time alerts
- Insights into user experience
Monitoring tools: Prometheus, Grafana, Datadog, New Relic, ELK Stack

5. Continuous Security (DevSecOps)

In the past, security was often added at the end of the development process. In DevOps, security is integrated from the beginning. This practice is called DevSecOps—short for Development, Security, and Operations.

Security checks are built into the CI/CD pipeline, and developers are trained to write secure code. Automated tools scan for vulnerabilities in real time.

Key benefits:
- Early detection of security flaws
- Lower risk of breaches
- Compliance with security standards
Security tools: Snyk, Aqua, Clair, Fortify

6. Continuous Feedback

Continuous Feedback means collecting real-time input from customers, end-users, and stakeholders. This feedback is then used to improve the product in the next cycle. It could come in the form of bug reports, customer reviews, surveys, or usage analytics.

Feedback is also encouraged within teams. Retrospectives, peer reviews, and performance metrics help teams learn and grow continuously.

Key benefits:
- Better user experience
- Product features that match real needs
- Faster response to change
Feedback tools: Jira, Slack, UserVoice, Microsoft Teams

7. Continuous Improvement

DevOps teams strive to improve with every sprint, release, and incident. This involves reviewing past performance, identifying bottlenecks, and experimenting with new ideas.

Key aspects include:
- Regular retrospectives
- Automation of manual tasks
- Process optimization
- Team learning and training
Continuous improvement is what turns a good DevOps team into a great one.

Benefits of a DevOps Mindset

Organizations that adopt a true DevOps mindset report several business and technical benefits:
1. Faster Time to Market
  Teams can deliver features, updates, and fixes faster and more frequently.
2. Better Collaboration
  DevOps breaks down silos, encouraging communication and shared responsibility.
3. Higher Quality and Reliability
  Frequent testing and monitoring improve the quality of software.
4. Stronger Security
  Security is integrated into every step, reducing risks and vulnerabilities.
5. Improved Customer Satisfaction
  Faster updates and quicker fixes enhance the customer experience.
6. Happier Teams
  Empowered teams that can deploy safely and frequently feel more motivated.
Challenges in Adopting DevOps

Adopting DevOps isn’t always easy. Some of the common challenges include:
- Cultural resistance: Changing mindsets takes time.
- Legacy systems: Old infrastructure may not support automation.
- Tool overload: Choosing and integrating the right tools can be complex.
- Lack of skills: Teams may need training in automation, cloud, and testing.
- Fragmented processes: Without a unified strategy, DevOps can become chaotic.
The key is to start small, show quick wins, and scale gradually.

DevOps Success Stories

Many companies have transformed their development culture through DevOps:
- Amazon deploys code thousands of times a day using DevOps automation.
- Netflix built its world-class streaming platform on DevOps principles.
- Etsy recovered from deployment issues by investing in a DevOps culture.
- Toyota (where I’ve had the honor to lead) leverages DevOps to enhance reliability, performance, and cost-efficiency in enterprise data platforms.
Final Thoughts: DevOps Is a Journey

DevOps is not a one-time project—it’s a journey of continuous transformation. It’s about creating a culture where development, operations, business, and security teams work together with shared goals, automated processes, and customer-centric thinking.

Yes, the tools matter. But more important is the mindset. When teams truly embrace DevOps, they move from delivering code to delivering value—faster, safer, and with greater impact.

So next time someone calls DevOps a “buzzword,” remember this: it’s not a trend, it’s a transformative philosophy—one that’s changing how the world builds and delivers technology.
May 29, 2025