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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
Why Developer Discipline Matters More Than Ever in the AI Era

Snyk CTO Danny Allan warns that the rapid adoption of generative AI coding tools could create a new wave of vulnerabilities across the global software supply chain. As developers increasingly rely on AI assistants to generate, modify, and deploy code, Allan said the industry risks entering what he called a “software security crisis” — one driven by speed, scale, and insufficient guardrails.

AI “vibecoding,” a growing trend in which developers use large language models (LLMs) to quickly produce functional but unverified code, is already reshaping how software is written. While these tools can accelerate delivery and boost productivity, Allan noted that they also tend to reproduce insecure patterns found in the data they were trained on. Without rigorous validation and context-aware governance, teams could be shipping vulnerabilities at a rate the industry has never seen before.

Allan argues that developers remain the first line of defense — but they need better, more integrated security feedback loops. Embedding security directly into developer workflows, he said, is critical to keeping pace with the surge of AI-generated code. That means using automated testing, dependency scanning, and real-time vulnerability detection as code is written, not after it’s deployed.

He also stressed that this moment is a turning point for DevSecOps: AI will be a powerful accelerator, but only for organizations disciplined enough to apply strong governance and maintain developer accountability. “The tools are getting smarter,” Allan said, “but so are the attackers.”

As AI transforms how software is built, Allan’s warning is clear — unless security evolves just as fast, the industry may be automating itself straight into the next major risk era.

October 31, 2025
Survey Surfaces Rising Tide of Vulnerabilities in Code Generated by AI

A survey of 450 IT professionals in the U.S. and Europe finds 69% of organizations have discovered vulnerabilities in code generated by artificial intelligence (AI) tools, with 20% reporting there has been a serious incident as a result.

Conducted by Sapio Research on behalf of Aikido Security, a provider of a platform of discovering vulnerabilities and protecting applications at runtime, the survey finds 92% of respondents are worried to some degree about vulnerabilities from AI-generated code, with 25% being seriously concerned.

Overall, the survey finds on average 24% of the code running in production environments has now been generated using AI tools. The survey, however, also makes it clear that the quality of code reviews being conducted remains suspect. Survey respondents report software engineers spend an average of 6.1 hours each week checking and triaging security tool alerts, with 72% of that time being wasted because of false positives. Nearly two-thirds of respondents (65%) said teams as a result bypass security checks, delay fixes or dismiss findings due to alert fatigue.

Aikido CISO Mike Wilkes said regardless of how code was written the pressure application development teams are under to deliver new features on time means that best DevSecOps practices will continue to be bypassed. The sad truth is that in the name of expediency many organizations are only paying lip service to best DevSecOps practices, he added.

It’s also unclear who is responsible for deploying flawed code, with more than half of respondents (53%) blaming security teams for not discovering vulnerabilities, compared to 45% that blame the developer and 42% who blame whoever merged the code.

On the plus side, 79% of respondents said their organization is relying more on AI to help fix vulnerabilities, the survey finds. Even so, an equal percentage also noted that it still takes longer than a single day to remediate critical vulnerabilities, with every organization having a backlog of issues that need to be addressed.

Despite these concerns, survey respondents remain optimistic about AI. A full 96% said they believe AI will write secure code eventually, but only 21% think that goal can be achieved without human oversight. A total of 90%, however, said they do expect AI to replace the need for humans to conduct penetration testing.

In general, AI is exposing software development flaws that have long existed, said Wilkes. The challenge is that at the current rate and scale applications are being built and deployed in the AI era, it’s now only a matter of time before these flaws manifest themselves in a way that creates major disruptions. The issue then becomes determining whether those disruptions warrant revisiting how software is constructed or simply viewing them as the cost of doing business in a multi-trillion dollar global economy that depends on flawed software, noted Wilkes.

Hopefully, the code being generated by AI coding tools is the worst it will ever be as additional advances are made. Until then, however, savvy DevOps teams would be wise to keep an eye on the amount of technical debt that is starting to pile up as application developers rely more on AI with each passing day.

October 29, 2025
GitHub Adds Platform for Managing AI Agents Embedded in DevOps Workflows

GitHub today launched a platform for managing artificial intelligence (AI) agent workflows across the entire software development lifecycle (SDLC).

Announced at the GitHub Universe 2025 conference, Agent HQ makes it possible to unify the management of AI agents developed by not just GitHub but also third-party providers, including Anthropic, OpenAI, Google, Cognition and xAI. As part of that effort, GitHub is also making it possible for DevOps teams to build and manage AI agents via Agent HQ that provides access to a mission control tool through which they can centrally manage AI agents and track metrics.

GitHub COO Kyle Daigle told conference attendees Agent HQ will make it simpler to employ AI agents that are capable of not just executing tasks but also reasoning about how best to organize and implement them. The goal is to make it possible to organize a fleet of AI agents in a way that reduces the overall amount of cognitive overhead that developers would otherwise encounter, he added.

For example, a plan mode in GitHub Copilot can now be used to build a step-by-step approach for building an application while at the same time being able to more granularly enforce controls, noted Daigle. Additionally, there is also now an AI agent to review code residing in a GitHub repository. Each repository can now have a tailored set of code reviews that can also be integrated with Code QL, an open-source semantic code analysis engine developed by GitHub.

GitHub is also adding branch controls that provide granular oversight over when to run continuous integration (CI) processes and other checks for agent-created code, identity management tools, one-click merge conflict resolution capabilities and integrations with Slack and Linear platforms used within DevOps workflows

Collectively, these capabilities are critical because as new tool chains are created using AI coding tools and agents that are embedded within the VS Code integrated developer environment (IDE), organizations need to be able to ensure quality is not being compromised, said Daigle. “Agents and developers working side by side is not something that’s coming,” he says. “It’s here.”

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said as more application development teams embrace AI we’re witnessing the arrival of a software development future that is seeing a shift from writing code in an editor to directing the agentic workflow.

GitHub Agent Hub, integrated with VS Code, makes it possible for developers to orchestrate multiple specialized agents instead of typing every line of code, he added. Developers instead define intent, guide priorities, and review the results, said Ashley. This represents the natural evolution of the coding environment that is managed by a single, intelligent hub, noted Ashley.

It’s still early days so far as the adoption of AI agents in software engineering workflows is concerned, but given the adoption of previous generations of AI coding tools, adoption is likely to accelerate. According to GitHub, AI is already being pervasively employed across the software development lifecycle, with 80% of new developers on GitHub leveraging Copilot within their first week.

The challenge now is moving beyond simply generating code to increasing the percentage of AI code that can be successfully deployed in a production environment. Much of the code currently generated by AI coding tools, unfortunately, is overly verbose in a way that adversely impacts application performance while simultaneously increasing technical debt. Sometimes referred to as AI slop, much of that code also contains known vulnerabilities that can be easily exploited.

Each organization will, of course, need to weigh the tradeoffs but, hopefully, in the age of agentic AI the overall quality of the code being generated by these tools is about to substantially improve.

October 28, 2025
Securing the AI Era: How Development, Security, and Compliance Must Evolve
The Code Boom and Its Paradox

We are witnessing an unprecedented shift in how software is built. With the rise of AI copilots, automated agents, and low-code platforms, code is being produced faster than at any point in history. What once took weeks can now be generated in minutes.

This is a remarkable advance, but it comes with a paradox. The explosion of code does not guarantee that software is more secure. In fact, the opposite is often true. More code increases the attack surface. AI introduces unpredictability and novel risks. Traditional approaches to securing the software development life cycle (SDLC) cannot keep up.

The core question for engineering and security leaders is no longer just how to build software quickly, but how to ensure that the speed of development is matched with the strength of security.

Maker and Checker: A Model That Must Evolve

Historically, software relied on a clear separation of roles. The developer was the maker, building features and writing code. The security analyst was the checker, reviewing work and catching mistakes before deployment.

This principle of dual control worked in a world of deliberate, slower releases. But in an AI-accelerated SDLC, it breaks down. The checker cannot keep pace with the volume of AI-generated code. Manual approvals and reviews become bottlenecks. Teams are forced to choose between velocity and safety.

The answer is not to abandon the maker and checker model. It must be rewired. Instead of gates that block progress, security becomes a set of guardrails. Developers are empowered with real-time feedback, while security engineers build the systems that enforce policy at scale.

The Developer’s New Role

Developers are no longer just authors of code. They are curators, reviewers, and defenders. Their responsibilities have expanded in three ways:
1. Curators of AI Output: Evaluating and integrating machine-generated suggestions, rejecting those that are insecure or opaque.
2. First Line of Defense: Operating with guardrails embedded within Integrated Developer Environments (IDEs) that flag unsafe code immediately.
3. Security-Aware Makers: Building features with an understanding that functionality and safety are inseparable.
The developer’s role is not to become a full-time security engineer, but to be empowered with guardrails and secure defaults that make the secure path the easy path.

The Evolution of Security Roles

The roles of security analysts and engineers must evolve as well. Analysts can no longer simply triage alerts and file tickets. Engineers cannot spend their time patching vulnerabilities one by one.

The future role is the Security Automator. Analysts become risk-ops engineers who tune signals and build dashboards that highlight the most important issues. Engineers become platform builders who embed policy-as-code and guardrails into the SDLC. Together, they design systems that intercept risks, automate remediation, and scale security across hundreds of developers.

Security is no longer about catching flaws at the end. It is about building systems that prevent flaws from being introduced in the first place.

The SDLC Becomes a Continuous Risk Loop

The linear SDLC is dead. The traditional flow of design, build, test, release, and operate no longer fits the reality of AI-driven development. Code is written and deployed continuously. Risks emerge at any point in the cycle.

The modern SDLC must be reimagined as a continuous risk loop.
1. Build: Code is produced by humans and AI.
2. Intercept: Guardrails catch issues in real time.
3. Triage: Risks are prioritized based on context and exploitability.
4. Remediate: Fixes are suggested or applied automatically.
5. Govern: Security and compliance teams ensure that these processes are aligned with organizational standards.
This loop is not theoretical. It must run continuously with every commit and every deployment.

Product Security 2.0: From Policies to Platforms

Product security has traditionally lived in documents and checklists. Policies are written, reviews are scheduled, and compliance evidence is gathered manually.

That approach cannot handle the speed of AI-driven software. Product security must shift from policies to platforms.
- Policies must become code that runs in pipelines.
- Guidelines must be replaced by guardrails that operate in IDEs and CI/CD.
- Evidence must come from dashboards that pull data directly from automated controls.
Product security becomes an engineering discipline. Its output is not documents, but systems that enforce security continuously and automatically.

Compliance That Improves Security

Compliance has long been viewed as a checkbox exercise. Audits are passed, but attackers do not care about certificates. Compliance alone does not prevent attacks.

Recent frameworks are changing that dynamic.
- PCI DSS 4.0 now requires organizations to manage all vulnerabilities, not just those rated critical.
- NIST SSDF emphasizes embedding secure practices throughout the SDLC.
- NIST CSF 2.0 added Govern as a new core function, making governance and accountability central to security.
These frameworks push organizations toward continuous, risk-driven security practices. Done correctly, compliance can become a forcing function for real security.

Metrics That Matter in the AI Era

Speed without safety is a false victory. Traditional metrics such as deployment frequency and mean time to recovery (MTTR) are not enough. Organizations must measure how well they manage risk at AI speed.

Key categories of metrics include:
- Detection: Mean time to intercept unsafe code, percentage of vulnerabilities caught pre-commit.
- Remediation: Mean time to remediate vulnerabilities, percentage of issues auto-remediated.
- Coverage: Percentage of repos and services protected by guardrails, policy drift between declared and enforced rules.
- Risk Reduction: Reduction in exploitable vulnerabilities, time to safe after each release.
The most effective organizations blend DORA metrics with these security metrics. This provides a holistic view of both velocity and safety.

A Call to Action

The AI era has redefined software. It has also redefined security. Developers are curators and first defenders. Security analysts and engineers are system builders and automators. The SDLC is a continuous risk loop. Product security has shifted from policies to platforms. Compliance frameworks are pushing organizations toward continuous, risk-driven practices.

The organizations that will thrive are those that measure not just how fast they can ship, but how safely they can ship.

The challenge is clear, and so is the opportunity. By embracing continuous guardrails, automation, and governance, we can ensure that software in the AI era is not only faster, but also stronger and more secure.
October 28, 2025
Don’t Reinvent the Wheel: A Developer’s Guide to AI Reusability

Every great innovation brings on new challenges, and AI is no exception. As technology leaders instruct their workforces to default to using AI in their day-to-day tasks and as AI workflows become increasingly critical to software engineering practices, a familiar challenge persists: reusability.

There are libraries, packages and shared components available for reuse with traditional software, but developers are currently being tasked with building AI workflows from scratch. Having multiple developers within one organization writing the same AI prompts and workflows is inefficient, wastes resources, can result in inconsistent outcomes, and potentially hinders wider adoption of AI.

The solution is applying standard software engineering principles to AI: Build once, reuse everywhere. In this post, we will cover some of the ways we have been thinking about the challenge of AI reusability at GitLab.

The Problem With Current AI Development Practices

Whether it’s a chat experience, a remotely executed AI workflow, or local code suggestions, developers have many opportunities to bring AI into their work.

As organizations encourage the increased use of AI in daily workflows, developers have largely been left to figure out how to utilize AI on their own. Broadly, there is very little guidance available on what works well in the context of an organization’s development practices.

As detailed in the soon-to-be-published 2025 GitLab DevSecOps Report, 85% of DevSecOps professionals agree that “Agentic AI will be most successful when implemented in a platform engineering approach.” When AI can be packaged and easily consumed by developers in a self-service way, it will help developers quickly onboard to AI experiences, share best practices, and maintain AI workflows via strong developer communities.

Organizations will be rewarded for their investment in this area with saved time for their development teams, cost savings from optimizing AI workflows, and the ability to deliver better results faster for customers. But what does it actually mean to make AI reusable?

Key Considerations for Making AI Reusable

There are several key considerations to keep in mind when creating reusable AI workflows. Let’s walk through the process of creating a reusable workflow using a common developer productivity issue as an example, and use this process to help developers fix failing pipelines more efficiently.

Step 1: Identify time-consuming developer workflows

One of the most common challenges developers face when using any CI/CD tool is the need to fix failing pipelines. This is a time-consuming task that requires looking through CI/CD logs, understanding the pipeline configuration, and iterating on potential solutions.

Step 2: Provide the appropriate context and permissions that will help focus AI models

By providing AI with lowest-level access to code repositories and CI/CD logs, along with a detailed system prompt, testing can be conducted to see how well AI handles different kinds of pipeline failures.

Step 3: Verify how well AI handles the task

As initial testing is conducted, access, context, tools, and prompts can be tweaked to produce better fixes. After testing AI’s ability to address pipeline failures, the next consideration is ensuring anyone can use this approach consistently.

Step 4: Define what hardware and tools can execute the AI workflow

What hardware is available to run this AI workflow reliably and with easy access? A common pattern is to use CI/CD jobs to remotely execute AI workflows, providing consistent tooling, prompts, and context.

For this particular workflow, CI/CD jobs enable the cloning of code repositories, installation of tools on underlying hardware, issuing API calls to AI models, and providing access tokens that grant the necessary permissions to suggest pipeline fixes back to a code repository. While CI/CD is one mechanism for executing AI workflows, organizations have many hardware options to consider.

Step 5: Version the workflow

Versioning becomes critical once the workflow is operational. For the pipeline fix workflow, consider what needs to be tracked: Which version of the AI model produces the best results for different failure types? What specific system prompt led to improvements in fix accuracy? Which tool versions are installed in the CI/CD environment?

Each tool used, the AI model version, hardware specification, system prompt, and even sample context data should be versioned together as a cohesive unit. This ensures that developers get a tested and reliable combination of all these elements.

Discoverability and Community

Once you have a strong process in place that others can benefit from, the next step is to make it findable. One suggestion is to apply an old idea to this new AI context: Software catalogs.

The idea behind a catalog is to make it easy for developers to search for what already exists. This should be the first step any development team takes before creating something new. As the old saying goes, let’s not reinvent the wheel.

Software catalogs have been a popular tool for promoting reusability across various areas of software development. For example, organizations have leveraged service catalogs to promote the reuse of APIs, CI/CD catalogs to share CI/CD best practices across the organization, and configuration catalogs to share infrastructure setups.

AI workflows need this same experience. Having a central location for all existing organizational information is crucial for facilitating AI adoption. It also helps promote community-driven development. Open source and inner source practices are not going to immediately change as a result of AI. When AI workflows are available to view and use, developers will contribute to improving the experience for themselves and others.

Whether you use a catalog experience or another way of centralizing AI workflows, it is important to think about how developers will find and contribute to already existing ideas for AI.

Treating AI workflows as reusable engineering assets rather than one-off implementations will accelerate AI adoption and improve consistency across development teams. Standard software engineering practices (modularization, packaging, versioning, and community contribution) apply directly to AI workflow development. Just as it is important to encourage AI usage throughout an organization, organizations must invest in frameworks that make it easy to discover and contribute to existing AI innovations.

October 24, 2025
The New Metrics of DevOps: Speed, Trust and Transparency

At swampUP 2025, Alan sits down with Aman Sardana and Vijay Kumar Soni to talk through one of the defining challenges of modern software delivery—how DevOps practices must evolve to accommodate the growing influence of artificial intelligence across the software development lifecycle. They explore the intersection of automation, security and scalability as organizations rethink their pipelines to handle AI-driven workloads and agentic development patterns.

The rapid integration of AI into coding, testing, and deployment workflows has significantly accelerated release velocity, but it has also increased complexity. Traditional CI/CD models are now being stress-tested by AI-generated code, model deployment requirements, and the need for continuous governance. The emerging consensus is that future-ready DevOps teams must combine automation with observability, policy enforcement, and risk management — effectively treating AI systems as first-class citizens within the software pipeline.

They also address the growing need for explainability and traceability as AI systems become embedded into infrastructure management and application delivery. Teams can no longer rely solely on deterministic systems; instead, they must design feedback loops that account for dynamic, probabilistic outcomes generated by AI. This requires enhanced collaboration between platform engineers, data scientists, and security teams to ensure consistency and reliability.

Ultimately, there’s a new reality for DevOps: speed alone is no longer the primary benchmark of success. In the AI era, the goal is responsible acceleration — maintaining trust, transparency, and control even as automation and AI agents transform how code, infrastructure, and models move from design to production. The next generation of DevOps will be defined not only by how fast organizations can deliver, but by how intelligently and securely they do it.

October 23, 2025
Opsera Adds AI Agent Capable of Analyzing Quality of Code Generated by AI Tools

Opsera today added an artificial intelligence (AI) reasoning agent to its platform that is specifically trained to surface in real time how DevOps teams are deriving value from investments in AI coding tools.

At the same time, Opsera is adding to its namesake platform for managing and automating DevOps workflows an ability to integrate with the Model Context Protocol (MCP) server developed by GitHub.

Finally, the company is also making an on-premises edition of its platform, dubbed Insights in a Box, available to IT teams that are not able or inclined to rely on a software-as-a-service (SaaS) platform for managing DevOps workflows. This self‑hosted edition of an existing Opsera Insights service that pulls data across the ecosystem, normalizes it, and surfaces key performance indicators (KPIs) that align with DevOps Research and Assessment (DORA) metrics.

Opsera CEO Kumar Chivukula said the Hummingbird AI Reasoning Agent makes it possible to determine which developers are generating code using AI tools that make it into a production environment. It tracks usage and adoption of AI coding tools, impact, value and productivity metrics to also surface optimization recommendations, he added.

That capability is critical because the more developers employ AI coding tools, costs start to rise as more tokens are consumed. The intelligence surfaced by Hummingbird AI Reasoning Agent can then be used to, for example, allocate more tokens to developers that are generating higher quality code, noted Chivukula.

Hummingbird AI agent understands context as it reasons across systems and tools to surface recommendations for improving productivity in natural language, said Chivukula. DevOps teams can chat with their own data to, for example, ask “why” something happened, “what” to do next, or “how” to improve performance. Those queries are then shared with large language models (LLMs) developed by Open AI and Anthropic, with the best answer then being shared back to the DevOps engineer, he added.

While AI coding tools are already widely adopted, generating more code doesn’t equate to more applications being deployed in a production environment. Many organizations are now generating more code but the developers reviewing that code don’t always understand how it was constructed. Often, that code can not only contain vulnerabilities, it is often overly verbose. As the volume of that code increases, the potential amount of technical debt being taken on increases as more inefficient code is added to an application environment.

In effect, Opsera is making a case for a DevOps platform that now uses AI to identify coding issues being created by other instances of AI.

It’s not likely that the AI coding genie is going back in the bottle any time soon, so the challenge now is determining how best to manage DevOps workflows when the amount of code of varying quality being generated has exponentially increased. Ultimately, the only way to achieve that goal is to rely on other instances of AI to provide greater visibility into software development lifecycles (SDLCs) where the number of AI coding tools being employed to write code is only going to continue to increase.

October 23, 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