The New Stack Podcast

CloudBees CEO Mo Plassnig is leading the CI/CD company through a major transformation as generative AI reshapes software development. Returning to CloudBees eight years after joining through its acquisition of CodeShip, which he co-founded, Plassnig says the emergence of generative AI renewed his interest in DevOps and the opportunities ahead.
His central concern is the dramatic increase in code generated by AI. Rather than focusing on predictions that autonomous agents will replace developers, Plassnig argues that enterprises face a more immediate challenge: safely managing, governing, and deploying an unprecedented volume of machine-generated software.
After meeting with Fortune 500 companies, public organizations, and global enterprises, Plassnig found a significant gap between AI hype and real-world adoption. Enterprises recognize the potential of agentic coding but must contend with complex process changes, governance requirements, and security concerns. His strategy is to reposition CloudBees as an AI-first company while rethinking how its Jenkins automation platform can support this new era of software development.
Learn more from The New Stack around the latest update with CloudBees and CI/CD:
CloudBees CEO: Why Migration Is a Mirage Costing You Millions
Why coding agents will break your CI/CD pipeline (and how to fix it)
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The AI industry has faced a growing enterprise dilemma: companies want access to powerful proprietary AI models without risking sensitive data or intellectual property, while AI labs want to protect their model weights from being exposed to customers. Traditionally, businesses had to choose between proprietary models with potential data-leakage concerns or open-weight models that lagged behind the frontier. Vast Data co-founder Jeff Denworth argues that a new approach can address both sides of the trust problem.
Vast Data’s DataEnclave uses Nvidia’s Confidential Computing technology to let enterprises run proprietary AI models securely on their own infrastructure, while preventing either the company’s data or the AI lab’s model weights from being exposed. Denworth says the timing reflects rapidly increasing enterprise AI adoption, particularly after agentic coding tools drove demand and usage. As AI agents create new requirements at the data layer, the podcast explores how enterprises are approaching AI, the security challenges involved, and the untapped potential of enterprise data.
Learn more from The New Stack around the latest in AI trust:
VAST Data tackles the enterprise AI trust gap
Google, Microsoft, and OpenAI join forces to help create AI’s missing trust layer
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Harness Field CTO Martin Reynolds joins The New Stack to talk about what happens after coding agents start opening pull requests faster than anyone can review them. He explains how he first saw the bottleneck during early GitHub Copilot trials, the three ways enterprises are coping with the volume now, and why Harness rebuilt its Code Repository and launched AI Code Review for agent traffic. The conversation also covers GitHub's recent outages, the software delivery knowledge graph behind Harness's reviewer, and how much of the delivery pipeline should stay deterministic.


Learn more from The New Stack around the latest in coding agents:
AI coding agents can write code, Crafting wants to help them ship it
Git real: AI agents aren't just for solo developers anymore
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Traces provide a detailed view of a request’s journey through data, microservices and applications, helping SREs pinpoint where failures occur and resolve issues faster. But while tracing can reduce downtime and developer burnout, collecting every trace creates its own problems. Storing massive volumes of data is expensive, can burden the systems being monitored and makes it harder to find the information that actually matters.
The solution isn’t abandoning tracing, but being smarter about what gets retained. Head sampling captures only a portion of traces upfront, while tail sampling evaluates completed traces and keeps those most valuable for troubleshooting. Dynamic sampling goes further by filtering repetitive or nearly identical traces before they overwhelm storage.
On The New Stack podcast, Sarah Hudspeth of Chronosphere, a Palo Alto Networks company, explains how teams can build a more effective tracing strategy. She breaks down how thoughtful sampling and observability design can turn tracing from a data-hoarding problem into a practical tool for production troubleshooting.
Learn more from The New Stack around the latest in tracing:
Sampling: the philosopher’s stone of distributed tracing
How OpenTelemetry Works: Tracing, Metrics and Logs on Kubernetes
Why Synthetic Tracing Delivers Better Data, Not Just More Data
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As AI evolves from conversational chatbots to autonomous agents, CPUs are becoming an increasingly important part of the infrastructure equation. In this episode, The New Stack speaks with Bhumik Patel of Arm and Mo Farhat of Google about how CPUs act as an “air traffic controller” for agentic workloads, handling orchestration, data preparation, semantic search, vector databases, code execution and API calls alongside GPUs and TPUs. Smaller AI models, including summarizers and evaluators, can also run effectively on CPUs for specialized tasks. 

As agents increasingly generate and execute code, secure sandboxing becomes critical. Google’s gVisor and GKE Agent Sandbox provide isolation and scalable environments, with the latter supporting up to 300 sandboxes per second per cluster. The discussion also explores efficiency and cost, with Google highlighting Axion’s price-performance and energy-efficiency advantages across different workload types. Ultimately, the shift toward agentic AI is creating a more diverse compute environment where CPUs, GPUs and TPUs each play complementary roles in delivering scalable, efficient AI applications.

Learn more from The New Stack around the latest in CPUs in the world of AI agents: 

AI Agents Will Eat Enterprise Software, Just Not in One Bite 

How to ground AI agents in accurate, context-rich data 

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