Advertise on podcast: The Platform Engineering Show

Agentic development platforms are pulling security, FinOps and AI infrastructure into one enterprise substrate, and this Platform Engineering Show episode traces the shift. Luca Galante, founder of PlatformEngineering.org, joins Alan Shimel to unpack Black Hat, AgenticCon and where internal developer platforms go next. About Luca Luca leads the PlatformEngineering.org community and its research arm Weave Intelligence. Consequently, he sees firsthand how large enterprises are shaping platform teams around AI and agents in 2026. Inside agentic development platforms Black Hat 2026 was agents all the way down, from agentic SOCs to identity for agents and automated vulnerability remediation. As a result, the market feels frothy and hard to navigate. Meanwhile, platform engineering still has room for new entrants because the discipline itself keeps expanding. In addition, the new reference architecture stacks agent infrastructure and path specification layers under the classic tooling planes. Therefore, agentic development platforms can serve humans and agents, mix deterministic CI CD with probabilistic skills and share substrate with other agentic workflows across the enterprise. Why this matters now Meanwhile, AgenticCon lands in San Francisco on February 24 and 25, Sydney and PlatformCon World Tour continue this fall, and PlatformCon 2027 is set for London on June 22 and New York on June 24. Consequently, House of Kube returns to KubeCon in Salt Lake City with roundtables and the unofficial opening party. Explore more DevOps coverage and the latest TechStrong TV interviews. Furthermore, the conversation previews Weave Intelligence reports on FinOps and AppSec for platform engineers and why tokenomics is quickly reshaping cost thinking. For more information please visit platformengineering.org

Alan Shimel and Luca Galante talk with Pankaj Gupta of Broadcom about why Platform Engineering 2.0 is emerging as the next evolution of internal platforms as AI changes how software is built, deployed, secured and governed. Gupta explains that platform engineering is no longer optional for modern application delivery, but the model is expanding beyond developer productivity and internal developer portals into a broader operating framework for AI-native enterprises. Rather than describing this shift as a rip-and-replace reset, the discussion frames Platform Engineering 2.0 as an evolution built on proven foundations such as platform as a product, golden paths, self-service, standardization and reduced cognitive load. The conversation explores how AI is changing the role of developers from writing code to validating, governing and moving code safely into production. As AI agents become new platform users, organizations will need platforms that support both human and non-human personas with guardrails, bounded permissions, observability and governance. Gupta, Shimel and Galante also examine why the platform audience is expanding to include security teams, compliance leaders, FinOps stakeholders, data scientists, machine learning engineers, business leaders and AI agents that may eventually outnumber human developers. A major theme is that AI creates revolutionary pressure, but IT teams still need evolutionary paths that protect existing investments. Platform Engineering 2.0 is presented around five pillars: AI-native platforms, multi-persona experiences, embedded FinOps, security shifting down into the platform and composable design. The episode also highlights why security must become more native, immutable and invisible to developers as AI increases vulnerability discovery, shadow IT, prompt injection, model poisoning and inference data leakage risks. For CIOs and engineering leaders, the takeaway is clear: Platform Engineering 2.0 gives organizations a practical way to stay in control of AI adoption, data, costs, security and delivery speed while still enabling experimentation, productivity and measurable business value.

Techstrong Group’s Alan Shimel sits down with Luca Galante to unpack the massive success of PlatformCon NYC and the shifting responsibilities of platform engineers in the agentic era. Galante explains that organizations are furiously realizing a flashy day-zero demo cannot scale into a functional, value-creating AI deployment without a robust, well-designed platform. Wielding AI as an indispensable tool, platform engineering teams are transcending traditional IT constraints to manage entire software supply chains and build the deterministic guardrails necessary to securely house future enterprise intelligence.

Alan Shimel and Luca Galante preview PlatformCon 2026, including the full virtual program, London and New York live days, workshops, panels and community events. Galante explains how this year’s agenda centers on AI, the agentic enterprise, platform engineering as an enabler of enterprise AI adoption and the role of practitioners in shaping the next phase of the discipline. The conversation also highlights keynote sessions, Kelsey Hightower, Caroline Wong, John Willis, enterprise panels, virtual access and new thought leadership from the platform engineering community.

The software development lifecycle is undergoing a seismic shift, and platform engineers are suddenly finding themselves at the very center of the agentic AI revolution. On this episode of the Platform Engineering Show, Techstrong Group’s Alan Shimel welcomes back platformengineering.org’s Luca Galante to discuss the highly anticipated PlatformCon world tour and the rapidly evolving intersection of developer experience, security, and AI governance. Bridging into a deep dive on the AI “vulnerability apocalypse” with industry veterans Rich Mogull and Mitchell Ashley, Shimel explores why a rock-solid platform engineering foundation is the only way organizations will survive this chaotic new era of infinite code generation.

While you were busy navigating the hype, AI just got real—transforming from a toy into a high-octane production tool that’s currently scaring the daylights out of the security world. Joining Alan today is Luca Galante, and they are breaking down why Q1 2026 is officially the year of the tech singularity, where “citizen developers” and a flood of AI agents are 1,000x-ing the pressure on our internal developer platforms. If you aren’t turning your “bumpy dirt road” infrastructure into an eight-lane superhighway with solid golden paths and automated guardrails right now, you’re not just lagging behind—you’re about to be replaced by someone who is.

Alan and Luca Galante discuss the upcoming KubeCon in Atlanta, the growth of the “House of Kube” community event, and the evolving role of platform engineering in the cloud-native ecosystem. They explore how enterprise adoption of cloud-native technologies has matured, with events now drawing more senior, business-focused audiences. The conversation highlights the intersection of AI and platform engineering, framing it as the next industrial revolution that could accelerate enterprise innovation and reshape the workforce. Alan and Luca conclude by emphasizing the importance of embracing AI and automation while previewing community gatherings that will continue these discussions at KubeCon.

Alan and Luca discuss the growing intersection between AI and platform engineering. Luca recaps insights from the recent Platform Compass event in Paris, where experts highlighted how AI is both transforming and depending on internal developer platforms. The two note Gartner’s prediction that most developer interactions will soon occur through AI agents, and they debate whether enterprises need entirely new AI-native platforms or can enhance existing ones. They conclude that AI is accelerating—not replacing—platform engineering, requiring organizations to build more scalable, “superhighway” infrastructures to support the AI-enabled future.

Alan and Luca discuss how bottlenecks are an inevitable part of platform engineering, drawing parallels to Eliyahu Goldratt’s Theory of Constraints and the Phoenix Project. They note that AI amplifies existing bottlenecks by accelerating throughput, making stable and standardized “golden paths” essential for scalability. The conversation concludes that complexity and new problems are inevitable, but advances in AI and eventually quantum computing will help organizations address them more holistically.

Alan Shimel, Luca Galante and Keith Townsend explore the evolving role of AI in platform engineering. They discuss the disconnect between AI capabilities and enterprise needs, emphasizing the importance of observability and standardization. The conversation highlights that current AI platforms are not enterprise-ready, leading to production challenges. They also address the need for scaling AI solutions and the gaps in vendor offerings, concluding with practical advice for organizations to adapt.

Platform engineering is undoubtedly one of the hottest trends in IT today, rapidly gaining traction among organizations deeply embedded in DevOps practices or cloud-native architectures. According to recent insights published by Futurum Group, platform engineering is increasingly appealing to vendors focused on Kubernetes, security, and artificial intelligence. The report highlights significant investment from leading technology providers, signaling strong momentum behind this emerging discipline. (Access the full report here.) Yet, amidst the buzz, an important question arises: Is platform engineering only beneficial for organizations already steeped in modern development practices? What about traditional, "brownfield" enterprises whose technological landscapes are anchored in legacy applications and conventional infrastructures? Can they too harness the advantages of platform engineering, or is this trend exclusively reserved for those already fully immersed in digital transformation? To better understand platform engineering's broader applicability, it's essential first to clarify precisely what it involves. Platform engineering provides development teams with self-service capabilities, simplifying infrastructure provisioning, operational management, and application deployments. Essentially, it abstracts complex infrastructure layers, allowing developers to focus on delivering software efficiently without grappling with operational minutiae. Naturally, organizations practicing DevOps and utilizing cloud-native infrastructure find immediate synergy with platform engineering. Such organizations typically have the foundational culture, tools, and processes that seamlessly integrate with platform engineering principles. Developers already accustomed to continuous integration and continuous deployment (CI/CD) workflows quickly appreciate the added convenience and operational efficiency. However, what about enterprises entrenched in legacy technology stacks, perhaps still midstream or just beginning their modernization journey? It's common to assume platform engineering might hold limited relevance for these companies, perceived as ill-equipped to quickly pivot to modern, dynamic software delivery methods. Yet, this assumption deserves careful reconsideration. Indeed, brownfield organizations often grapple with considerable complexities and inefficiencies precisely because of their legacy systems. They face challenges related to manual processes, prolonged deployment cycles, inconsistent infrastructure management, and cumbersome operational workflows. Ironically, these are the very issues platform engineering is designed to address. By adopting platform engineering, even partially, traditional organizations can significantly streamline their operational overhead. Centralizing infrastructure management and standardizing deployment pipelines reduce inconsistencies and human errors, thus directly benefiting even legacy applications. Moreover, introducing platform engineering can incrementally drive modernization by creating a structured, supportive environment that gradually integrates contemporary practices and tools. For instance, organizations reliant on older applications might leverage platform engineering practices to automate routine infrastructure tasks, such as server provisioning or configuration management. These automations significantly reduce the administrative burden and operational costs, allowing IT teams more bandwidth to engage strategically with modernization projects. Furthermore, platform engineering provides a scalable, secure, and repeatable approach to infrastructure management—attributes crucial for brownfield environments undergoing gradual transformation. The Futurum Group report emphasizes Kubernetes, security, and AI as pivotal technology areas where platform engineering adds substantial value. Interestingly, these areas are highly relevant regardless of an organization's technological maturity. Kubernetes, for instance, can be introduced incrementally alongside legacy systems to manage containerized workloads progressively. Platform engineering, in this scenario, becomes the gateway to harnessing Kubernetes’ benefits without necessitating immediate, wholesale adoption. Similarly, security enhancements provided through platform engineering are universally beneficial. Automating security practices ensures consistency and compliance, particularly valuable for enterprises operating under stringent regulatory frameworks, a common reality among traditional businesses. Furthermore, platform engineering's automation capabilities can significantly reduce the risk exposure associated with manual security configurations. AI, too, offers transformative potential irrespective of the organization's modernization stage. Platform engineering can enable controlled, incremental adoption of AI-driven tools that enhance monitoring, predictive analytics, and operational efficiency, providing immediate value even in legacy-heavy environments. Nevertheless, the transition towards platform engineering in traditional enterprises isn't without challenges. Cultural resistance, skillset gaps, and initial investment requirements can pose barriers. Successful adoption in these contexts necessitates thoughtful strategic planning, phased implementation, and strong organizational commitment. In conclusion, platform engineering is emphatically not just for the "kool kids." While DevOps-savvy, cloud-native organizations undoubtedly have a head start, brownfield enterprises stand to gain significantly by adopting platform engineering principles. By providing operational efficiencies, facilitating incremental modernization, and enhancing security and scalability, platform engineering can be an invaluable asset in the broader journey toward digital transformation—regardless of an organization's current technology landscape. (Download the comprehensive Futurum report here for deeper insights.)

In the latest episode of the Platform Engineering show, Alan and Luca dive into the evolving world of platform engineering, exploring why it’s gaining traction and how it’s reshaping team structures with new, specialized roles. They unpack how security is becoming a core part of platform strategy—and take a look at the growing influence of AI on observability and system intelligence.

Join host Alan Shimel and co-host Luca Galante in this episode of The Platform Engineering Show as they dive into key challenges in platform engineering. They discuss the pitfalls of simply rebranding DevOps teams, the need for upskilling rather than renaming roles, and the difficulty of finding true platform product managers. The conversation explores how organizations can move beyond reliance on “unicorn” leaders by building structured training programs and fostering a product mindset across teams. Luca also shares insights from recent industry events, highlighting the growing maturity of platform engineering and the need for executive buy-in to drive successful adoption.

Many platform teams never successfully leave the planning phase before they lose funding. Many fall apart after building something large, time consuming, and expensive that nobody uses. The Minimum Viable Platform framework provides a structure and process to start fast and small in a way that lets you demonstrate value to stakeholders and ensure buy in and sponsorship for your platform engineering initiative. It is also the secret sauce to ensuring future adoption, and roll out to the true enterprise platform engineering initiative.

The platform-as-a-product mindset is perhaps the key differentiator between platform engineering and neighbor disciplines like DevOps, SRE, or I&O. In this episode, we will cover where it came from, and dive into the why, the what, and the established best practices.