D2DO306: Platform Engineering in the Agentic Era (Sponsored)
Jad Elzane and Miles Gray from VMware by Broadcom discuss how platform engineering evolved from DevOps to address developer cognitive overload, and how Platform Engineering 2.0 must now accommodate AI agents as consumers alongside human developers, requiring new security guardrails and observability controls.
Summary
The episode explores the transition from DevOps to Platform Engineering (PE), framed as a correction rather than a replacement. DevOps' "build it, run it" model created cognitive overload for developers by burdening them with infrastructure concerns, compliance requirements, and tasks like Kubernetes management that should belong to platform teams. Platform Engineering emerged to solve this by treating the platform as a product with standardized "paved roads" or "golden paths" that abstract away complexity while maintaining developer freedom.
Key statistics from the 2025 DORA report show that 90% of organizations are already using some form of internal developer platform (IDP), and 76% have established dedicated platform teams—significantly ahead of analyst predictions. Benefits include higher deployment velocity, faster time-to-market, and happier developers and ops teams through standardization.
A significant shift in VMware's strategy is discussed: moving from an opinionated, proprietary approach to embracing open standards and CNCF projects. This was driven by customer demand and accelerated by Broadcom's acquisition, which provided investment and leadership focus. VMware now positions itself as the substrate (infrastructure layer) rather than dictating the entire stack, integrating projects like ArgoCD rather than competing with them.
The major forward-looking discussion centers on Platform Engineering 2.0, where the primary consumer is no longer just human developers but increasingly AI agents. This introduces new considerations: agents don't have job security concerns like humans, can exhibit unexpected creative solutions to problems, potentially circumvent guardrails, and require different security and isolation strategies. The platform must provide rock-solid foundations, clear API surfaces, and modern observability to both enable AI productivity and protect against unintended consequences. The speakers emphasize that platform quality directly multiplies AI effectiveness—poor platforms lead to poor agent outcomes, while excellent platforms make AI a significant multiplier of developer capability.
About this episode
Platform engineering forms the foundation for developers to build on, and you shouldn’t be surprised that folks from VMware have been thinking about platforms for a long time. In today’s episode, sponsored by Broadcom, Ned and Kyler discuss the current state and future of platform engineering with guests Jad El-Zein and Myles Gray. They cover<a class="excerpt-read-more" href="https://packetpushers.net/podcasts/day-two-devops/d2do306-platform-engineering-in-the-agentic-era-sponsored/" title="ReadD2DO306: Platform Engineering in the Agentic Era (Sponsored)">... Read more »</a>
Key Insights
- DevOps successfully shifted testing and operations left into the development lifecycle but ultimately overburdened developers with cognitive load, including Kubernetes management and compliance concerns that should be handled by platform teams rather than individual developers.
- Platform Engineering inverts the DevOps shift-left approach with a 'shift-down' strategy, embedding security, observability, governance, and compliance consistently into the platform itself rather than making them developer responsibilities.
- The primary consumer of Internal Developer Platforms is rapidly shifting from humans alone to include machines and AI agents, fundamentally changing what security guardrails, observability, and control mechanisms platforms must provide.
- AI agents as platform consumers require entirely different constraint models than human developers because they lack job security concerns and institutional knowledge, and can creatively devise solutions outside intended boundaries (the paperclip maximizer problem).
- Platform quality directly determines AI effectiveness: when the underlying platform provides clear APIs, searchable and composable components, and reliable data, AI agents perform significantly better and faster, making the platform central to agent outcomes.
- VMware shifted from its traditional opinionated, proprietary platform approach to embracing open standards and CNCF projects after Broadcom's acquisition, driven by customer demand for standards-based, vendor-neutral solutions.
- 90% of enterprises are already using some form of IDP and 76% have dedicated platform teams according to DORA 2025, demonstrating mainstream adoption years ahead of analyst predictions.
- The platform team's role as a product manager requires balancing developer freedom with enterprise compliance and security—treating the platform as a living product with feedback loops rather than a static set of policies.
Topics
Transcript
One of the things we have to consider moving forward is that the developer isn't necessarily your primary consumer, isn't your buyer for this product you're putting out there anymore, the product being the IDP. Machines are equally the consumer.. Welcome to Day 2 DevOps where the DevOps is in the details. I'm Ned Belovance and I'm joined by my bedazzled co-host, Kyler Middleton. Hey, Ned. In today's sponsored episode, we are discussing the current state and future of platform engineering with our friends from Broadcom. Platform engineering forms the foundation for developers to build on, the current state and future of platform engineering with our friends from Broadcom. Platform engineering forms the foundation for developers to build on, and…
Full transcript available for MurmurCast members
Sign Up to AccessMore from The Everything Feed - All Packet Pushers Pods
TNO071: The Network Team Is Drowning. Is AI the Life Raft? (Sponsored)
Rekha Shenoy and Irfan Kimji from Backbox discuss how the exponential growth of vulnerabilities (49,000 CVEs annually) has made manual network operations unsustainable, and how AI-powered automation can help network teams manage patches and security updates at scale while maintaining human control and oversight.
HN840: How to Make a Technology Buying Decision
Sean Morgan, a research director at Deloro Group, discusses how technology buying decisions should extend beyond engineering specifications to include business alignment, ROI calculations, and understanding total cost of ownership. Engineers must shift from viewing IT as a cost center to positioning it as a business enabler by connecting technical decisions to revenue impact and organizational objectives.
IPB207: Flying Blind: Monitoring Might Not See IPv6
The IPv6 Buzz hosts discuss critical gaps in IPv6 monitoring across enterprise networks, highlighting that many monitoring platforms lack IPv6 awareness, vendor parity, and advanced analytical capabilities. They emphasize that while basic IPv6 data ingestion has improved, sophisticated features like cross-protocol event correlation, extension header analysis, and device identity tracking remain significant industry challenges.
N4N063: Link Layer Discovery Protocol
Link Layer Discovery Protocol (LLDP) is a standardized Layer 2 protocol that enables network devices to announce information about themselves to directly connected neighbors, facilitating network topology discovery and device identification in multi-vendor environments. The protocol uses Ethernet frames with special multicast destination MAC addresses to ensure frames don't propagate beyond immediate neighbors, and includes mandatory TLVs (Type-Length-Values) like chassis ID, port ID, and TTL alongside optional ones for extended information.
TCG083: Superintelligence for Everyone: Who Actually Holds the Power?
Three technology experts discuss Mark Zuckerberg's manifesto on distributed superintelligence, examining whether his promises of universal access and individual empowerment align with infrastructure realities. They conclude that while decentralized AI is theoretically safer than centralized control, the manifesto fails to account for human complexity, existing inequalities, and the enormous capital requirements that will likely concentrate power rather than distribute it.