The New Stack Podcast

The New Stack

The New Stack Podcast is all about the developers, software engineers and operations people who build at-scale architectures that change the way we develop and deploy software.

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35 episodes
Avg 28 min
English

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#1653
Friday · 36 min
AI agents are getting more complex. Developers shouldn't have to babysit them.
AI coding agents are becoming more autonomous, thanks to advances in harness engineering that enable them to identify and correct mistakes before human review. In the latest episode of The New Stack podcast, Vanitha Kumar, Marketing Technology Director and Technical Partner at Thoughtworks, explains how automated tests, feedback loops, and guardrails allow agents to handle longer running tasks with less human intervention while working in parallel.
#1652
Wednesday · 32 min
Beyond LLMs: How Small Decision Models Make AI Agents Smarter and Faster
In this episode of The New Stack podcast, recorded at AGNTCon + MCPCon in San Jose, Nick Lucchesi speaks with Mike Chambers, Senior AI Developer Experience Engineer at AWS, about how small decision models could make AI agents faster, more efficient, and cost-effective.
#1651
Tuesday · 24 min
The Future of Agentic AI: Why Open Standards and Interoperability Matter
In this episode of The New Stack podcast, recorded at AgentCon + MCPCon in San Jose, host Nick Lucchesi speaks with Manik Surtani, co-founder and CTO of the Agentic AI Foundation (AAIF), about the growing importance of open standards, interoperability, and vendor-neutral governance in agentic AI.
#1650
Tuesday · 29 min
Building AI Agents That Don't Fail: Lessons From Orkes
Turning a promising AI demo into a reliable, production-ready application requires more than powerful models. At AGNTCon + MCPCon in San Jose, Jeu George, Co-Founder and CEO of Orkes, joins The New Stack’s Nick Lucchesi to discuss the challenges of building mission-critical agentic applications and the infrastructure needed to support them.
#1649
October 30 · 25 min
Can agent standards keep up with the companies building them?
At AGNTCon + MCPCon in San Jose, Mazin Gilbert, executive director of the Agentic AI Foundation, joins The New Stack’s Frederic Lardinois to discuss the rapid growth of agentic AI and the importance of open standards in shaping its future.
#1648
October 29 · 24 min
AWS's Hydro team wants the compiler to catch coding agents' mistakes
As AI agents generate increasingly large volumes of code, traditional code reviews are struggling to keep pace. Shadaj Laddad, research lead for the Hydro team at AWS, believes the solution lies in automated correctness checks that operate outside an AI agent’s control.
#1647
October 28 · 26 min
Why MCP servers should tell agents what to do next
James Ward, AWS Fellow and technical committee member of the Agentic AI Foundation, joins The New Stack's Matt Burns at AgntCon+MCPCon 2026 to discuss the rapid evolution of developer and agent experiences. Ward shares how building for AI agents has transformed engineering velocity, highlighting that development roadmaps have collapsed from months down to days. Because agents require clear guidance to reach their goals, practices that were once optional for human developer experience—such as rob
#1645
October 7 · 25 min
Kubernetes won the orchestration wars. Its teams are still fighting.
Kubernetes has become the dominant container orchestration platform, used by more than 80% of container users, but widespread adoption hasn’t eliminated tensions between developers and operations teams. Developers value Kubernetes for its self-service capabilities and ability to move quickly, while operations teams prioritize stability, governance and controlled change. In this episode, HPE Fellow David Estes explains how those competing priorities are becoming more challenging as applications s
#1644
October 6 · 30 min
The CNCF is graduating projects faster than ever. AI agents are helping with the due diligence.
Open-source technology has played a foundational role in the AI boom, giving companies the flexibility to scale unprecedented computing workloads across providers and hardware. OpenAI, for example, used Kubernetes as early as 2018 to balance compute across its own data centers, AWS and Azure — years before ChatGPT brought generative AI into the mainstream.
#1643
October 5 · 20 min
Dynatrace wants its AI agents to fix problems instead of adding alerts
Dynatrace sells software that monitors companies’ apps and infrastructure and wants its AI agents to make engineers’ jobs easier. An agent that gets in the way is a bad agent and should be dropped, said Sean O’Dell, a principal product marketing manager at the company.
#1641
October 1 · 31 min
Bit Cloud’s next chapter starts after the AI builds your app
For many developers, turning an AI-generated prototype into maintainable software requires more than generating code—it requires infrastructure, collaboration, testing and review. In this episode of The New Stack podcast, Bit Cloud founder and CEO Ran Mizrahi discusses Bit Cloud 2.0 and Hope, the company’s AI builder, and how they connect application creation with the work that follows.
#1642
September 30 · 23 min
CloudBees just committed to an AI-first pivot. Here's why it matters for enterprise DevOps teams
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.
#1640
September 23 · 30 min
A third option is emerging in the fight over AI and your data
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 t
#1639
September 7 · 26 min
Drowning in AI pull requests: Harness's field CTO on code review and a Git repo built for agents
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 Harnes
#1638
September 3 · 31 min
How to find failures without drowning in tracing data
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.
#1637
August 11 · 26 min
Why CPUs still matter in the age of AI agents
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 al
#1636
July 31 · 35 min
Why Doist Says Less AI Can Deliver More
Doist CTO Gonçalo Silva says AI is reshaping software development, but success depends on restraint rather than rapid feature expansion. Instead of chasing every AI capability, Doist prioritizes “subtraction over addition,” removing features that fail to deliver lasting value despite development investment. After experimenting with nearly 20 AI concepts, the company found success with Ramble, an AI-powered voice task capture feature, while remaining model-agnostic through rigorous testing and ev
#1635
July 30 · 30 min
Why your company should (try to) build its own AI SRE
As AI coding agents accelerate software development, they also create new challenges for site reliability engineers (SREs), who are increasingly responsible for debugging systems that no single human fully understands. In this episode of The New Stack podcast, Sam Farid and Nate Heinrich of Chronosphere, a Palo Alto Networks Company, argue that AI agents should also be used for root-cause analysis, helping teams diagnose failures more quickly as model capabilities continue to improve.
#1634
July 23 · 32 min
Nvidia
In this episode with The New Stack's Frederic Lardinois, NVIDIA’s Joey Conway says advances in AI over the past year have dramatically improved the capabilities of local models, making them practical for enterprise and personal use alongside frontier cloud models. Rather than replacing large models, Conway envisions a “system of models” where specialized local models handle routine, cost-sensitive, or privacy-focused tasks, while larger frontier models tackle more complex reasoning. He explains
#1633
July 14 · 19 min
Meet Brain, the AI that decides when Azure is officially down
In this episode, Mark Russinovich, CTO of Microsoft Azure revealed Brain, the AI-powered AIOps system that continuously monitors Azure’s health, detects incidents, identifies root causes, and increasingly automates responses such as pausing problematic deployments and notifying affected customers. Built on Azure Resource Graph, Brain creates a real-time digital twin of Azure, mapping dependencies across hundreds of services, data centers, and regions. Although Brain predates the generative AI bo

Showing 1–20 of 35 episodes