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Category: Video Interviews

  • Making the Case for AI in Testing, Security and Delivery—Not Just Coding

    Making the Case for AI in Testing, Security and Delivery—Not Just Coding

    Harness CEO and co-founder Jyoti Bansal unpacks a core challenge facing modern software teams: while AI is dramatically increasing the amount of code being written, it is also putting unprecedented pressure on everything that happens after code is committed.

    Bansal argues that much of the industry’s AI focus has centered on the developer inner loop—AI-assisted coding on laptops—while overlooking the outer loop, where most of the real work lives. Testing, security scanning, deployment verification, compliance checks, release orchestration, and cost management consume the majority of engineering effort in large organizations. As AI accelerates code generation, those downstream processes become bottlenecks unless they are equally automated and modernized.

    This is where Bansal frames Harness’ vision of “AI for everything after code.” Rather than relying on generic copilots, he describes an approach built around purpose-designed AI agents that handle specific DevOps, testing, security, and FinOps tasks. These agents operate with deep contextual awareness, drawing from a software delivery lifecycle knowledge graph that reflects an organization’s infrastructure, policies, pipelines, and operational constraints.

    Crucially, Bansal emphasizes that AI should not replace human judgment in production environments. Instead, AI is used to generate deterministic, auditable orchestration—pipelines, tests, rollout strategies, and safeguards—that humans can review, approve, and govern. This human-in-the-loop model is especially important for large and regulated enterprises, where reliability, security, and blast-radius control matter as much as speed.

    They also explore how rising code volumes—especially from AI-generated code—can actually reduce delivery throughput and quality if the outer loop is neglected. Without strong automation, observability, and guardrails, more code simply means more vulnerabilities, more failures, and more rework.

    The message is clear: AI’s real impact on DevOps will be determined not by how fast teams write code, but by how intelligently they automate, secure, and govern everything that comes next.

  • How Low-Code Is Reshaping DevOps Automation and Governance

    How Low-Code Is Reshaping DevOps Automation and Governance

    Jon Scolamiero breaks down how low-code platforms are becoming an increasingly important part of the modern DevOps ecosystem. As organizations grapple with rapid shifts brought on by AI, vibe-coding practices, and an explosion of tooling, Scolamiero argues that low-code isn’t a shortcut—it’s a way to bring structure, automation, and transparency back into software delivery.

    He explains that the core mechanics of DevOps don’t disappear in a low-code environment. Pipelines still need to deploy reliably, infrastructure still needs to be provisioned, and applications still need to be secured and monitored. What changes is how teams interact with those systems. Low-code abstracts much of the repetitive orchestration work so teams can focus on governance, quality, and outcomes instead of plumbing. For organizations struggling with shadow AI, fragmented workflows, or inconsistent platform engineering practices, this abstraction can be the difference between chaos and clarity.

    Scolamiero also touches on the growing divide between U.S. and EU compliance expectations. While the EU pushes ahead with strong regulatory frameworks—including around AI transparency—the U.S. market often lags in political will but compensates with heavier requirements in highly regulated industries like federal, defense, and finance. That gap puts new pressure on DevOps teams to understand not just how software is built, but how every dependency enters the environment. Software supply chain security, Scolamiero notes, is no longer a niche concern—it’s a foundational requirement.

    Looking ahead, he warns that AI-driven development may soon trigger a high-profile security incident unless organizations adopt responsible governance practices. For DevOps teams, the message is clear: abstraction and automation are powerful, but only if paired with visibility, policy, and a strong understanding of the end-to-end delivery chain.

  • Why Junior Developers Remain Essential in the Age of AI

    Why Junior Developers Remain Essential in the Age of AI

    Raj Sethi, senior vice president and go-to-market leader for software development lifecycle (SDLC) at GlobalLogic, pushes back on the narrative that generative AI will eliminate the need for junior developers. He argues that while AI can generate boilerplate code, tests and scaffolding, modern software delivery still depends on human judgment, critical thinking and a deep understanding of how systems are designed, built and evolved over time.

    Sethi notes that recent layoffs and hiring slowdowns are less about AI replacing humans and more about organizations recalibrating after over-hiring and then scrambling to understand how AI actually fits into their productivity models. Despite headlines about “60–70% of code written by GenAI,” he points out that teams still need people to design prompts, review what AI produces, validate correctness, and reason about architecture, algorithms and performance. If organizations skip that layer of human oversight—especially at the junior level—they risk creating an unsustainable wave of technical debt.

    He also challenges how junior talent is being prepared and evaluated. In an era where learning resources, courses and tools are democratized, Sethi urges aspiring developers to treat education as continuous, not transactional. Degrees alone aren’t enough; portfolios, open source contributions and visible problem-solving matter more than ever. He encourages students to focus on fundamentals—math, algorithms, linguistics, systems thinking—and to use AI as a collaborator rather than a crutch.

    For DevOps and software delivery teams, the future isn’t about replacing junior engineers with AI, but about elevating their role. Junior developers will spend less time on repetitive plumbing and more time reviewing AI-generated output, refining specs, improving quality and learning how to orchestrate systems. In that world, the organizations that invest in growing their next generation of engineers—not just in buying tools—will be the ones that actually benefit from AI at scale.

  • Humans in the Loop: Governing AI Agents Across the SDLC

    Humans in the Loop: Governing AI Agents Across the SDLC

    AI agents are moving from novelty to necessity in software delivery—and with that shift, the developer’s role is changing. Emilio Salvador, vice president of strategy and developer relations for GitLab, explores what happens when every engineer works alongside a small “team” of agents: some personal, some shared across squads, and others assigned to specific lifecycle tasks. The takeaway: DevOps will look less like a linear pipeline and more like an orchestrated system where humans set intent and policy while agents execute, verify and report.

    Salvador lays out a practical model for “AI guardians”—specialist agents that continuously check security, quality and compliance as code and configs move through the system. Above them sits a layer of “meta-agents” that coordinate work, escalate issues and interact just like team members (email/Slack/tickets), while keeping humans firmly in the loop for approvals and accountability. No one’s shipping enterprise software on autopilot.

    Salvador also gets real about today’s bottlenecks. Most teams fixate on AI for code generation, but the real drag lives elsewhere: brittle CI/CD, slow security checks, noisy incidents and manual release chores. The guidance is to instrument AI across the entire SDLC—pipeline optimization, policy enforcement, test generation, SCA/secret scanning, rollback logic—not just at the editor prompt. Otherwise, you’ll write code faster only to hit the same wall faster.

    On governance, the risk isn’t just “shadow AI” tools; it’s fragmented models and agent sprawl. What’s needed is a single source of truth for software assets and activity, plus clear policies for which agents can do what, where, and with which data. Expect multi-model strategies, layered verification (agents auditing agents) and stronger observability for both pipelines and AI behavior.

    Finally, we touch modernization: AI will help refactor legacy apps and accelerate adaptation cycles. The horizon isn’t “more software,” it’s faster evolution—with DevOps teams measured by how intelligently they balance speed, safety and scale.

  • The DevOps Impact of API-First Development

    The DevOps Impact of API-First Development

    Balaji Raghavan, chief technology officer at Postman, breaks down key findings from the company’s 7th Annual State of the API Report — offering an inside look at how APIs are reshaping software development and DevOps practices worldwide.

    Raghavan notes that APIs have evolved from tactical integration tools into strategic enablers of digital transformation. According to the report, nearly every software organization now relies on APIs to connect distributed systems, streamline development, and accelerate product delivery. What’s changing, he explains, is the scale and complexity of API ecosystems — and how DevOps teams are adapting to manage them securely and efficiently.

    They highlight several trends emerging from the survey: the rise of API-first development, the growing importance of governance and lifecycle management, and the increasing use of AI to design, test, and monitor APIs. For DevOps teams, this means embedding API observability and automation directly into their pipelines to improve performance and reliability.

    Raghavan also discussed how teams are addressing the challenges of security, version control, and dependency sprawl as APIs become more interconnected across multi-cloud environments. He stressed that visibility and collaboration remain key — developers, platform engineers, and security teams must work from a shared source of truth to keep up with the pace of API-driven innovation.

    Ultimately, the report’s message is clear: APIs are no longer supporting players in the DevOps ecosystem — they’re the backbone of modern software delivery. The organizations that invest in automation, governance, and developer experience will be the ones that thrive as API complexity continues to grow.

  • Vibe Coding vs. Spec-Driven Development: Finding Balance in the AI Era

    Vibe Coding vs. Spec-Driven Development: Finding Balance in the AI Era

    David Yanacek, Sr. Principal Engineer, AWS Agentic AI, dives into the rise of vibe coding versus traditional spec-driven development. As generative AI continues to transform how code is created, developers are moving faster than ever, but not always with the same level of structure, review, or intent that defined earlier software engineering practices.

    Yanacek explains that vibe coding—writing code by “feel” with the help of large language models and AI copilots—has opened the door to rapid prototyping and experimentation. However, it also raises new questions around reliability, reproducibility, and long-term maintainability. Without clearly defined specifications and governance, teams risk introducing technical debt and vulnerabilities at a scale that traditional code review processes were never designed to handle.

    By contrast, spec-driven development enforces design discipline and predictable outcomes, but it can feel slow and rigid in fast-moving environments. The challenge, Yanacek notes, is to strike a balance between the agility of AI-assisted creativity and the rigor of structured engineering. That means applying DevOps principles, such as automation, observability, and feedback loops, not just to deployment, but to the AI-assisted coding process itself.

    Yanacek also highlights the evolving role of developers and security engineers as AI becomes embedded in the software lifecycle. In his view, the organizations that succeed will be those that treat AI as a collaborator rather than a crutch, leveraging it to scale quality, not just speed.

    Innovation is accelerating, but without structure and security, that speed could come at the expense of trust and control.

  • How Data, Empathy and Visibility Are Redefining DevOps Maturity

    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.

  • Why Developer Discipline Matters More Than Ever in the AI Era

    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.

  • The New Metrics of DevOps: Speed, Trust and Transparency

    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.

  • How DevOps Is Evolving for the Age of Intelligent Automation

    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.