The New Stack Podcast

Broadcom continues to expand its role as a major contributor to cloud-native open source, particularly within the Cloud Native Computing Foundation (CNCF) ecosystem. Its recent donation of Velero—originally developed by VMware—to the CNCF Sandbox reflects a strategic move to foster broader community trust and collaboration. By shifting governance away from vendor control, Broadcom aims to position Velero as a truly community-driven data protection standard for Kubernetes environments, encouraging wider adoption and contribution. 

At the same time, the company is reinforcing its position as a full-stack Kubernetes provider across both cloud-native and private cloud environments. Despite Kubernetes’ dominance, many organizations still struggle with its complexity. Broadcom is addressing this by focusing on lifecycle management, long-term support, and deep integration with existing infrastructure like vSphere. 

In a podcast recorded at KubeCon + CloudNativeCon Europe 2026, Dilpreet Bindra emphasized that open source success comes not just from code contributions, but also from relinquishing control to empower the broader ecosystem and drive sustainable innovation. 

Learn more from The New Stack about the latest developments around Velero: 

Broadcom donates Velero to CNCF — and it could reshape how Kubernetes users handle backup and disaster recovery 

How AI Search Is Supporting Artistic Freedom 

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In this episode of The New Stack Makers, Nimisha Asthagiri of ThoughtWorks explores why many AI initiatives stall between proof of concept and production. A key issue is that organizations focus on speed—asking how to move faster—rather than rethinking what new capabilities AI actually enables. Successful companies take a systems-thinking approach, investing in organizational literacy and aligning teams around meaningful use cases instead of retrofitting AI into existing workflows.

Asthagiri highlights that core engineering practices are ফিরে to prominence. As AI-generated code increases, so does the risk of “cognitive debt,” where developers lose understanding of their own systems. To counter this, teams are reviving fundamentals like test-driven development, mutation testing, observability, and zero-trust security, especially as autonomous agents contribute to production code.

She also introduces the concept of “dark code”—AI-generated code that may never be used—and argues for more intentional lifecycle management, including ephemeral code. Ultimately, the focus shifts from code itself to specifications, context management, and disciplined engineering practices.

 

Learn more from The New Stack around the latest about system-thinking approaches: 

System Two AI: The Dawn of Reasoning Agents in Business 

A practical systems engineering guide: Architecting AI-ready infrastructure for the agentic era 

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Graduating within the CNCF marks a major milestone for an open source project, signaling not just technical maturity but strong governance, security practices, and widespread adoption. Kyverno, a Kubernetes policy engine, reached this stage after five years — becoming only the 35th project to progress from sandbox to graduation. As co-founder Jim Bugwadia explains, incubation reflects production readiness and adoption, while graduation validates the project’s long-term sustainability and governance rigor.

Originally built to help teams manage Kubernetes complexity through declarative policies, Kyverno has evolved alongside the ecosystem. Its shift to the Kubernetes-native Common Expression Language (CEL) and rising demand driven by AI workloads have expanded its user base beyond regulated industries to mainstream enterprises. With over three billion downloads, it underscores the growing need for automated policy enforcement across development, security, and operations teams.

Commercially, Nirmata maintains a clear boundary between open source and enterprise offerings, focusing on remediation and advanced management. While only 2–5% of users convert, that small percentage becomes meaningful at Kyverno’s scale.

Learn more from The New Stack around the latest about Kyverno:

Simplify Kubernetes Security With Kyverno and OPA Gatekeeper

Using the Kyverno CLI to Write Policy Test Cases

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At the MCP Summit in New York City, AWS’s Luca Chang, a Bedrock team member and MCP specification maintainer, discussed the rapid rise of the Model Context Protocol (MCP) as a standard for connecting AI models and agents to tools and data. He explained that MCP’s development is shaped by a diverse group of maintainers who collaboratively prioritize features, balancing major challenges with smaller enhancements that can unlock creative new capabilities. This breadth of perspectives prevents groupthink but makes prioritization difficult, as many ideas compete for limited bandwidth.

Chang highlighted the role of large organizations like Amazon in advancing open source projects. AWS contributions such as Tasks and Elicitations emerged from internal efforts to map cloud services to MCP, revealing gaps in the protocol. Rather than contributing for speed, AWS focuses on real customer use cases, contributing only when clear needs arise. Chang also noted growing demand for MCP servers, while expressing caution about overly specialized, agent-specific implementations that could limit broader interoperability.

Learn more from The New Stack around  the latest in Model Context Protocol (MCP) becoming a standard for connecting AI models and agents to tools and data: 

Model Context Protocol: A Primer for the Developers

Beyond the vibe code: The steep mountain MCP must climb to reach production

https://thenewstack.io/model-context-protocol-evolution/

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AtKubeCon Europe 2026,Jorge Palmaoutlined how Microsoft is advancing AI operations across cloud and edge environments. He demonstrated an agent capable of diagnosing, mitigating, and explaining application issues in minutes, highlighting the growing role of agentic operations in Kubernetes.

Palma emphasized that recent progress in tools likeAzure Kubernetes ServiceandAzure Archas made edge AI more practical by bridging cloud and on-prem systems. Kubernetes now acts as the unifying layer, while fleet management automates deployments that previously required manual GitOps workflows.

To address fragmentation in inference engines, Microsoft introducedAI Runway, a standardized API that allows teams to swap underlying engines without changing workflows.

Security remains a core challenge. Palma advocates for tightly scoped, temporary permissions and policy validation for agents, enforced through tools like the Agent Governance Toolkit. This reflects a broader shift: applying cloud-native principles—portability, abstraction, and policy control—to manage the unpredictable nature of AI workloads.

Learn more from The New Stack about the latest around advancing AI operations across cloud and edge environments

The Future of AI: Hybrid Edge Deployments Are Indispensable

AI Is Coming to the Edge, but It Will Look Different

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