The New Stack Podcast
- Épisodes
- 300
- Parution
- quotidienne
- Dernier épisode
- 13 Nov 2026
À propos du podcast
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. For more content from The New Stack, subscribe on YouTube at: https://www.youtube.com/c/TheNewStack
Épisodes 300
DuckLake 1.1 solves the absurd complexity of data lakes at AI scale
DuckLake is simplifying data lakehouse management by replacing complex metadata files with a familiar SQL-based approach. In this episode of The New Stack Podcast, Pedro Holanda, lead DuckLake developer and …
Notes de l’épisode
DuckLake is simplifying data lakehouse management by replacing complex metadata files with a familiar SQL-based approach. In this episode of The New Stack Podcast, Pedro Holanda, lead DuckLake developer and principal engineer at DuckLake, discusses how the open-source table format reduces complexity, improves performance, and makes large-scale data management more accessible.
With the release of DuckLake 1.1 alongside DuckDB 2.0.0 LTS, Holanda explains how storing metadata in a relational database eliminates the need to navigate countless files to locate and query data. By combining Parquet storage with SQL catalogs, DuckLake streamlines data access while addressing common challenges such as excessive small files, inefficient queries, and costly maintenance.
The conversation also explores DuckLake’s innovative inlining capabilities, which consolidate smaller datasets to improve efficiency, and its ability to support analytics without massive server infrastructure. As AWS integrates DuckDB into its broader analytics strategy, DuckLake represents an important step toward simplifying data portability, reducing operational overhead, and enabling more efficient analytics for developers and AI agents.
Learn more from The New Stack around the latest developments around DuckDB:
Why MotherDuck refuses to fork DuckDB
DuckDB: Query Processing Is King
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The Context Problem: What's Really Breaking AI Agents at Scale
As AI models become more powerful and affordable, enterprises still face a major obstacle to realizing the full potential of AI agents: the context gap. Without access to accurate, relevant information, even …
Notes de l’épisode
As AI models become more powerful and affordable, enterprises still face a major obstacle to realizing the full potential of AI agents: the context gap. Without access to accurate, relevant information, even the most advanced models struggle to deliver meaningful results.
In this episode of The New Stack podcast, Sri Desikan, an executive at Elastic, explains why providing AI agents with the right context is essential for moving beyond impressive demonstrations to reliable enterprise automation.
Simply connecting agents to enterprise data through MCP servers isn't enough. Feeding massive amounts of information into large language models is costly, inefficient, and slow.
Elastic's approach centers on building an AI index that organizes enterprise data, metadata, and relationships, enabling agents to retrieve relevant information faster while reducing token consumption.
Desikan also explores how precomputed context can help prevent outdated information from influencing AI decisions. The conversation examines the implications for data privacy, agent autonomy, and the future of enterprise AI adoption.
Learn more from The New Stack around the latest developments in bridging the enterprise AI context gap:
The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context
Why enterprise AI breaks without metrics discipline
Why enterprise AI keeps stalling — and how data streaming could unlock it
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The Next Essential Developer Skill: Building AI Agents
As AI agents transform software development, developers face a fundamental shift in the skills they need to succeed. In this episode of The New Stack podcast, recorded at AGNTCon and MCPCon in San Jose …
Notes de l’épisode
As AI agents transform software development, developers face a fundamental shift in the skills they need to succeed. In this episode of The New Stack podcast, recorded at AGNTCon and MCPCon in San Jose, Frederic Lardinois speaks with Angie Jones, Vice President of the Agentic AI Foundation, about the evolving developer role and the challenges of adopting AI across engineering organizations.
Drawing on her experience leading AI enablement at Block, Jones explains how embedding agent-friendly practices into development workflows helped drive widespread adoption. She emphasizes the growing importance of system design, logical thinking, and building agentic systems rather than simply using AI coding tools.
The conversation also explores opportunities for junior developers, the role of open standards like MCP, and how AI-generated contributions are reshaping open source maintenance.
As organizations navigate this transition, Jones highlights the importance of cultural change, collaboration, and developing new skills to build secure, reliable AI agents that extend beyond software development.
Learn more from The New Stack around the latest developments around the evolving role of the developer:
The 3 roles AI agents play in your developer platform
AI Will Steal Developer Jobs (But Not How You Think)
10 New Jobs for Burned-Out Software Developers
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New CNCF report: 75% of cloud-native developers ship code with AI. Jonathan Bryce explains why.
AI adoption is accelerating among cloud-native developers, who are leading the charge in integrating artificial intelligence into software development. According to the Cloud Native Computing Foundation’s …
Notes de l’épisode
AI adoption is accelerating among cloud-native developers, who are leading the charge in integrating artificial intelligence into software development. According to the Cloud Native Computing Foundation’s (CNCF) Q3 2026 State of Cloud Native Development report, 21.2 million developers—41% of the global developer population—work with cloud-native technologies. Of those, 75% use AI to write and ship code, compared with just 50% of non-cloud-native developers.
In this episode of The New Stack podcast, CNCF Executive Director Jonathan Bryce discusses the rapid growth of the cloud-native ecosystem, evolving developer trends, and the expanding role of open source in driving innovation.
Bryce highlights CNCF’s project maturity process, from sandbox to incubation and graduation, and explores emerging technologies such as KubeRay and Agent Substrate alongside graduating projects including KubeVirt, Karmada, and Kubeflow.
The conversation also examines the Kubernetes AI Conformance program and why understanding open-source technologies is increasingly essential for organizations seeking flexibility, innovation, and competitive advantage in an AI-driven world.
Learn more from The New Stack around the latest developments around the State of the Cloud and the evolving cloud native ecosystem.
Cloud native application challenges: installing the walking skeleton
Cloud Realities Are Slowing AI Ambitions
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Why Agentic AI Needs a New Approach to Data Infrastructure
As AI agents transform how businesses access and process data, traditional siloed infrastructure is struggling to keep pace. In this episode of The New Stack podcast, recorded at AGNTCon + MCPCon 2026, Frederic …
Notes de l’épisode
As AI agents transform how businesses access and process data, traditional siloed infrastructure is struggling to keep pace. In this episode of The New Stack podcast, recorded at AGNTCon + MCPCon 2026, Frederic Lardinois speaks with Bianca Lewis, Executive Director of the OpenSearch Software Foundation, about why unified, open source data infrastructure is becoming essential for agentic AI.
Lewis explains how OpenSearch is evolving beyond traditional search and observability into an AI data infrastructure platform that enables organizations to scale workloads, reduce costs, maintain data sovereignty, and avoid vendor lock-in.
The conversation explores how AI agents introduce new observability challenges, requiring organizations to monitor API interactions, trace agent behavior, and capture contextual data to improve accuracy and performance.
With production clusters nearly doubling over two years and a growing community of 3,500 contributors, OpenSearch is rapidly expanding its capabilities. Lewis also discusses the foundation's evolving commercial ecosystem, enterprise support programs, and the importance of community-driven innovation in shaping the future of AI infrastructure.
Learn more from The New Stack around the latest developments around agentic AI and data infrastructure:
Agentic infrastructure operations begin with accurate, reliable infrastructure data
The rise of agentic AI on Kubernetes: unleashing the new infrastructure layer
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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 …
Notes de l’épisode
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, Principal Technologist 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.
Kumar also explores the rise of AI factories, which provide standardized infrastructure for model selection, workload management, and cost optimization. Much like Kubernetes simplified microservices management, AI factories aim to make deploying and managing AI agents easier at scale.
However, greater autonomy introduces security risks as agents gain access to more systems, data, and tools. Kumar emphasizes the importance of guardrails, controlled environments, and human approvals to mitigate these vulnerabilities. She also previews Thoughtworks’ Technology Radar, Volume 35, highlighting emerging AI practices alongside established engineering disciplines, including penetration testing and path-to-production understanding, that are becoming increasingly critical in the AI era.
Learn more from The New Stack around the latest developments around engineering disciplines, including penetration testing and path-to-production understanding:
Why AI engineering needs old-school discipline
Agents are rewriting the rules of security. Here’s what engineering needs to know.
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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 …
Notes de l’épisode
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.
Unlike large language models (LLMs) that generate responses through multiple processing steps, decider models quickly evaluate information and return probability-based decisions. Chambers explains how these models can improve agent performance by routing requests to appropriate LLMs, enforcing safety guardrails, and determining when agents should retrieve information from memory.
He also shares experiments using AWS Strands Agents and Strands Decider to reduce unnecessary memory lookups and keep agent context relevant. While these models offer significant efficiency advantages, Chambers emphasizes the importance of evaluation, monitoring, and human oversight, particularly for high-risk decisions involving financial transactions.
Looking ahead, Chambers envisions a future where developers combine specialized AI models rather than relying exclusively on LLMs, paving the way for more capable, adaptable agents and applications.
Learn more from The New Stack around the latest developments around small decision models and its impact on AI:
SLMs vs. LLMs: Why Smaller AI Models Win in Business
AWS launches a local answer to TypeSafe’s Jev decision model
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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 …
Notes de l’épisode
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.
With 305 member organizations and 10 projects, AAIF is expanding its ecosystem through new additions including Anthropic’s AgentSkills, OpenAI’s OpenResponses, Hugging Face’s OpenM, and Alibaba’s OpenSandbox. Surtani explains how these projects help developers build more portable, trustworthy AI systems while reducing dependence on individual vendors.
The conversation explores unresolved challenges in agent memory, identity, authentication, observability, and verifying work performed across multiple agents. Surtani also discusses the foundation’s evolving approach to supporting early-stage open-source projects and why the agentic AI stack may never fully stabilize.
Looking ahead, he predicts that AI agents will transform commerce, from purchasing decisions to advertising, making open protocols and industry collaboration increasingly essential.
Learn more from The New Stack around the latest developments around open standards and interoperability in agentic AI:
The Agentic Web: How AI Agents Are Shaping the Web’s Future
6 agentic knowledge base patterns emerging in the wild
Five AI rivals just backed a shared plugin standard. Here's why it matters for developers.
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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 …
Notes de l’épisode
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.
George explains how Orkes Conductor helps developers orchestrate AI agents, manage failures, enforce security guardrails, and maintain audit trails for accountability and compliance. He also introduces ParticleDB, an agentic database designed to simplify enterprise data access while keeping sensitive information within company cloud environments.
Using examples from financial services and e-commerce, George explores when businesses should rely on AI agents, deterministic workflows, or human oversight. He emphasizes the importance of designing systems that anticipate failures, recover automatically, and provide visibility into every agent action.
Looking ahead, George predicts cheaper AI models, faster processing, and greater adoption of privately deployed agents will accelerate enterprise AI innovation.
Learn more from The New Stack around the latest developments around building mission-critical agentic applications and the infrastructure needed to support them in the AI era:
Why Agentic AI Orchestration Is Key To Managing AI Complexity
A practical systems engineering guide: Architecting AI-ready infrastructure for the agentic era
The rise of agentic AI on Kubernetes: unleashing the new infrastructure layer
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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 …
Notes de l’épisode
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.
With 4,300 conference registrants and a rapidly expanding membership, the foundation is bringing together technology giants, startups, nonprofits and enterprises to address challenges around scalability, security, interoperability and governance. Gilbert explains how protocols like MCP are fueling a new economy of startups while enabling competing companies, including OpenAI, Anthropic, Google and Microsoft, to collaborate on shared infrastructure.
The conversation explores the foundation’s approach to adopting new open source projects, expanding global participation through partnerships with organizations like the Gates Foundation, and encouraging meaningful community contributions. Gilbert also highlights emerging opportunities in agentic commerce, identity, trust, observability and physical AI.
Looking ahead, the foundation aims to accelerate standardization and expand its global community, ensuring agentic AI systems can scale securely without costly fragmentation.
Learn more from The New Stack around the latest developments around growth of agentic AI and the importance of open standards in shaping its future:
The Architect's Guide to Understanding Agentic AI
The Agentic Web: How AI Agents Are Shaping the Web's Future
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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 …
Notes de l’épisode
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.
Speaking at AGNTCon + MCPCon, Laddad explained how Hydro, an open source Rust framework for distributed systems, uses compile-time checks to catch bugs before code runs. Inspired by Rust’s borrow checker, Hydro aims to provide shared, domain-specific guardrails that help developers maintain reliability even as agents produce larger, more complex code changes.
The team is also exploring safeguards for recurring challenges such as idempotency, ensuring retried requests don’t execute twice. Laddad also highlighted Infinity, an open source runtime designed to support massive agent concurrency by suspending idle agents awaiting external operations. Developed jointly by AWS and UC Berkeley, Hydro and Infinity represent efforts to make AI-driven software development more scalable, efficient and trustworthy.
Learn more from The New Stack around the latest developments around front end frameworks:
AI-generated Rust compiles perfectly. That's the scary part.
Do Frontend Frameworks Still Matter? A Thread
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Why MCP servers should tell agents what to do next
Many Model Context Protocol (MCP) servers function as basic APIs, returning data without guiding AI agents on what to do next. AWS Principal Developer Advocate James Ward believes MCP servers should play a more …
Notes de l’épisode
Many Model Context Protocol (MCP) servers function as basic APIs, returning data without guiding AI agents on what to do next. AWS Principal Developer Advocate James Ward believes MCP servers should play a more active role in agent workflows, providing context, recommendations and actionable next steps.
Speaking at AGNTCon + MCPCon in San Jose, Ward explained how better observability and evaluation can improve agent performance. Because MCP lacks built-in trace IDs, AWS uses session identifiers to track tool calls across workflows, helping developers understand how agents interpret requests, identify inefficiencies and complete tasks.
By analyzing these interactions, teams can refine tool descriptions, introduce more efficient capabilities and measure improvements through evaluations. Ward also emphasized that MCP isn't always the best integration approach, noting that AWS evaluates alternatives such as command-line interfaces and skills.
Ultimately, Ward advocates applying established software engineering practices to agentic AI: observe workflows, identify problems, test improvements and continuously optimize how humans and AI agents collaborate to accomplish tasks.
Learn more from The New Stack around the latest developments in rapid evolution of developer and agent experiences:
The 3 roles AI agents play in your developer platform
Your agent context needs a development lifecycle
AWS open-sources an AI agent it says is 45% cheaper than Claude Code and Codex
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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 …
Notes de l’épisode
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 spread across hybrid and edge environments and AI inference workloads increase demand for GPU scheduling and rapid capacity scaling.
He also warns that excessive reliance on microservices can create unnecessary “architecture bloat.” AI, however, could help solve some of the complexity it is creating. Estes sees opportunities to use AI automation to monitor Kubernetes clusters, analyze logs, automate infrastructure tasks and potentially predict outages. With tools such as HPE OpsRamp supporting multicloud and on-premises environments, AI-driven operations could ultimately help developers and operations teams manage increasingly complex Kubernetes infrastructure more effectively.
Learn more from The New Stack around the latest developments in HP Enterprise:
HPE's AI agents cut root cause analysis time in half
AI Is Quickly Making IT Teams and Developers From Invisible to Indispensable
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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 …
Notes de l’épisode
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.
CNCF Executive Director Jonathan Bryce sees a powerful feedback loop emerging between AI and open source. Early AI companies relied on open-source tools because commercial products couldn’t support the scale and complexity of their work. As those companies contribute back, enterprises across industries gain access to increasingly sophisticated AI infrastructure.
AI is now accelerating open-source development itself. CNCF has used agents to assist with project due diligence, helping move projects through its graduation process faster. As AI agents increase development speed and infrastructure demands, Bryce argues that open-source communities will be critical for addressing challenges including multi-cloud computing, hardware diversity, guardrails and software capable of operating at agent speed.
Learn more from The New Stack around the latest open source and AI developments:
Open-source AI is just "4 months behind" closed frontier models - and 10x cheaper
Is AI killing open-source software?
Open source maintainers are drowning in AI-generated pull requests. Enterprise teams are next.
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Dynatrace wants its AI agents to fix problems instead of adding alerts
Dynatrace's view is that AI apps should be monitored like any other app. They still run alongside older systems, including mainframes, and when something breaks, teams need to know what it cost and why it …
Notes de l’épisode
Dynatrace's view is that AI apps should be monitored like any other app. They still run alongside older systems, including mainframes, and when something breaks, teams need to know what it cost and why it happened. Their Bluebox agent, introduced in July, compares a team’s code with production data and proposes a fix as a pull request. Engineers can review it or let it run without them. The goal is fewer alerts, delivered in the tools developers already use, such as Claude and Codex, rather than in a separate dashboard.
Learn more from The New Stack around the latest around Dynatrace's latest development:Dynatrace’s new agents can reveal the single hardest part of AI operations
Beyond automation: Dynatrace unveils agentic AI that fixes problems on its own
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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 …
Notes de l’épisode
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.
Mizrahi explains how reusable components, authentication and integrations can help teams avoid duplicating work, reduce token costs and simplify code review. The conversation also explores collaboration across developers, designers, product managers and business teams, all working from shared building blocks.
Mizrahi demonstrates how development workflows can extend to mobile devices, including staging previews, build checks and code review. The discussion covers working with existing codebases, tools such as Claude Code and Cursor, and moving standard application code beyond Bit Cloud. Demonstrations show an Instagram-style prototype evolving into an application architecture and how testing can catch problems before production. Throughout, Mizrahi emphasizes building on existing work so each application can become a foundation for the next.
Learn more from The New Stack around the latest in integrating AI into Development workflows
Your AI Workflow Is Missing a Composable Architecture
Lessons from 2 Years of Integrating AI into Development Workflows
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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 …
Notes de l’épisode
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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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 …
Notes de l’épisode
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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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 …
Notes de l’épisode
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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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 …
Notes de l’épisode
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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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 …
Notes de l’épisode
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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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 …
Notes de l’épisode
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 evaluations.
Internally, developers use a variety of AI coding tools rather than standardizing on one platform, while Doist OS—a companywide AI assistant with nearly 100 shared skills—helps employees across all functions work more effectively. Silva also outlined Doist’s approach to AI-powered automations, separating AI-driven workflow generation from deterministic execution to improve reliability and reduce token costs. Throughout its AI strategy, the company emphasizes purposeful features, privacy, transparency, and continuous improvement, ensuring AI enhances user productivity without compromising product quality or trust.
Learn more from The New Stack around developer productivity:
Developer Productivity in 2025: More AI, but Mixed Results
Optimizing for Developer Productivity Creates a Winning DevEx
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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 …
Notes de l’épisode
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 ofThe New Stackpodcast, Sam Farid and Nate Heinrich of Chronosphere argue that AI agents should also be used for root-cause analysis, helping teams diagnose failures more quickly as model capabilities continue to improve.
Rather than immediately purchasing a commercial solution, they recommend organizations first build an in-house AI SRE. The process of documenting systems, dependencies, and operational knowledge creates valuable context that enables AI agents to troubleshoot effectively while improving institutional knowledge. Although Chronosphere offers its own AI SRE platform, the hosts emphasize that building an internal prototype helps teams understand their needs before evaluating vendor tools. As AI-generated code becomes more common, organizations that invest in mapping their systems and leveraging AI for operations will be better equipped to reduce downtime and support increasingly complex software environments.
Learn more from The New Stack around AI SREs:
5 ways SRE AI agents are set to augment human capabilities
The Future of AI in SRE: Preventing Failures, Not Fixing Them
AI Reliability Engineering: Welcome to the Third Age of SRE
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Nvidia
In this episode with The New Stack Agents, 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 …
Notes de l’épisode
In this episode with The New Stack Agents, 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 that organizations can fine-tune smaller open models using domain-specific data, creating expert AI agents that reflect the specialized roles found within businesses.
NVIDIA supports this ecosystem through open models, training tools, and software such as NeMo, Dynamo, and Nemotron. Conway also highlights the growing importance of agentic harnesses, which give AI models access to tools, memory, and iterative workflows, significantly improving performance and reducing costs. Looking ahead, he expects AI orchestration to become increasingly important, with intelligent routing systems selecting the right model for each task based on complexity, cost, latency, and data governance requirements, enabling enterprises to balance performance, security, and efficiency.
Learn more from The New Stack around NVIDIA's latest updates in AI:
Palantir and Nvidia want to change who owns government AI
Nvidia's best model is now live
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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 …
Notes de l’épisode
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 boom, years of data engineering, standardized service-level indicators (SLIs), and machine learning laid the foundation for today’s capabilities.
Brain combines standardized SLIs, service-specific monitoring, and third-party signals to detect anomalies, while ML models dynamically establish service baselines and correlate outages with software rollouts. Microsoft says automated notifications have reduced customer support tickets by four to six times, with 80–90% of Brain-covered services receiving notifications within 15 minutes, often in under five. The company is also layering LLM-powered agents, called Triangle, on top of Brain to streamline incident routing and eventually enable AI agents to autonomously troubleshoot and remediate outages.
Learn more from The New Stack around the latest in Microsoft Azure:
Meet Brain, the AI that decides when Azure is officially down
Microsoft's pitch to enterprises: Ditch Azure Repos for GitHub, despite its rocky reliability record
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What comes after attention? This startup says it already knows.
Subquadratic is beginning to back up its ambitious claims with benchmarks and third-party validation for its SubQ 1.1 Small model, which uses its proprietary Sparse Attention (SSA) architecture to dramatically …
Notes de l’épisode
Subquadratic is beginning to back up its ambitious claims with benchmarks and third-party validation for its SubQ 1.1 Small model, which uses its proprietary Sparse Attention (SSA) architecture to dramatically improve long-context performance. Rather than comparing every token to every other token, SSA selectively processes relationships, enabling near-linear scaling while maintaining high accuracy across context windows of up to 12 million tokens. The company reports near-perfect retrieval performance, competitive coding and reasoning benchmarks, and compute savings of up to 1,000x at maximum context lengths.
Rather than targeting frontier models immediately, Subquadratic is focusing on enterprise customers that need efficient analysis of massive datasets. The current model was built by replacing the dense attention mechanism in an existing open-weight model and then continuing long-context pretraining. Looking ahead, the startup plans to release a larger mid-tier model while continuing research into "zero attention" architectures that could eliminate attention mechanisms altogether, with the long-term goal of surpassing today's transformer-based AI models in both efficiency and capability.
Learn more from The New Stack around cloud spending:
The context window has been shattered: Subquadratic debuts a 12-million-token window
What comes after attention? This startup says it already knows.
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“The harness is where the hard work is”: Harness bets on agents that enterprises can trust in production
Harness has introduced Autonomous Worker Agents, a new capability that allows enterprises to replace rigid CI/CD pipeline scripts with AI agents that can deploy applications, run tests, and perform security …
Notes de l’épisode
Harness has introduced Autonomous Worker Agents, a new capability that allows enterprises to replace rigid CI/CD pipeline scripts with AI agents that can deploy applications, run tests, and perform security scans while operating under existing governance, security, and audit controls. Unlike Harness' existing expert agents, which assist developers with coding and pipeline creation, Worker Agents autonomously execute pipeline tasks within customer-controlled infrastructure. Agents are defined using simple Markdown files, draw context from the Harness Software Delivery Knowledge Graph, and run in sandboxed environments with scoped permissions and policy enforcement.
Harness also provides built-in audit trails that record prompts, decisions, and outcomes, along with token budgets and approval gates to control AI costs. The launch includes an Agent Marketplace featuring Harness-managed, certified partner, and community-built agents. CEO Jyoti Bansal said production AI agents require far stronger safeguards than coding assistants, positioning Harness' governance and knowledge graph as key differentiators. Looking ahead, the company envisions fully autonomous software engineering, where AI agents manage the software lifecycle while humans oversee high-risk decisions.
Learn more from The New Stack around AI software delivery:
AI won't speed up software delivery - nothing has
How to solve the AI paradox in software development with intelligent orchestration
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Public cloud vs. on-prem: Summit on where each workload belongs
More than two decades after AWS helped usher in the public cloud era, many organizations are reassessing whether a cloud-first strategy still delivers the cost and operational benefits it once promised. While …
Notes de l’épisode
More than two decades after AWS helped usher in the public cloud era, many organizations are reassessing whether a cloud-first strategy still delivers the cost and operational benefits it once promised. While hyperscalers such as AWS, Azure and Google Cloud have built enormously successful businesses, cloud spending has become a growing concern for customers as usage expands and costs continue to rise.
On this episode of The New Stack Makers, Summit’s Byron Dill argues that many enterprises have become overly reliant on public cloud infrastructure, using it for workloads that may be better suited to private environments. Rather than treating the cloud as a one-size-fits-all solution, Dill advocates for a more segmented approach that places workloads where they make the most sense based on cost, security and management requirements.
The conversation draws parallels to the rapid adoption of AI, where organizations often discover unexpected costs after implementation. Dill explores when repatriating workloads from the public cloud to private infrastructure can reduce expenses, simplify data management and improve control, while examining the costs, timelines and industries best positioned to benefit from a private cloud strategy.
Learn more from The New Stack around cloud spending:
How to Cut Cloud Waste Without Constricting Developer Productivity
AI agents need to spend money — Stripe and iWallet are building the rails
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Gusto Cofounder: An AI agent that runs payroll, HR, and benefits without waiting to be asked
Gusto is betting that small businesses need more than another AI assistant. The company’s new product, Gusto Cofounder, is designed to act as a proactive business partner that helps owners manage and grow their …
Notes de l’épisode
Gusto is betting that small businesses need more than another AI assistant. The company’s new product, Gusto Cofounder, is designed to act as a proactive business partner that helps owners manage and grow their companies, drawing inspiration from the traditional mom-and-pop partnership that co-founder and CTOEddie Kimwitnessed growing up. Unlike reactive chatbots, Cofounder can take action across payroll, HR, benefits, scheduling, insurance, and accounting workflows by leveraging data already stored within Gusto.
Users interact with the platform through text messages or Slack, while a consent framework ensures access to sensitive payroll and employee data remains tightly controlled. Businesses can grant explicit permissions and gradually increase autonomy as trust is established. The platform also integrates with third-party tools such as Google Workspace, enabling it to gather data, perform calculations, run payroll, and communicate results automatically.
Kim said the product was built by a five-person team in just eight weeks using Claude Code, which he believes demonstrates how AI is expanding software creation beyond traditional engineering roles. Looking ahead, Gusto plans to add more integrations and eventually enable customers and developers to share reusable, industry-specific business automations.
Learn more from The New Stack around how AI is expanding software creation beyond traditional engineering roles:
How AI Is Reshaping Software Engineering: Key Takeaways From DeveloperWeek 2025
AI and the Future of Code: Developers Are Key
The Engineer in the AI Age: The Orchestrator and Architect
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WeAreDevelopers is coming to the US to give unsung developers a bigger voice
WeAreDevelopers, the Berlin-based developer conference founded in 2015, has grown into a major global event, attracting 15,000 developers from over 70 countries each year. In 2026, it expands beyond Europe with …
Notes de l’épisode
WeAreDevelopers, the Berlin-based developer conference founded in 2015, has grown into a major global event, attracting 15,000 developers from over 70 countries each year. In 2026, it expands beyond Europe with new editions in San Jose, California, and Bengaluru, India. Co-founder and CEO Sead Ahmetovic says the conference was created to give developers a stronger voice in an industry where marketers, salespeople, and entrepreneurs often receive more recognition.
He believes developers, despite being less vocal, build the products that power the modern world. The event began as a small meetup that quickly gained popularity, filling a gap between highly specialized technical gatherings and broader business-focused conferences. Former GitHub CEO Thomas Dohmke highlights another benefit: giving developers a platform to share the stories behind their work and inspire peers.
Discussing the future of software development, Dohmke predicts AI agents will handle much of the coding, while developers focus on managing ideas, prompts, and workflows. Ahmetovic agrees, arguing that developers will remain essential, spending less time typing code and more time thinking, orchestrating, and creating new solutions.
Learn more from The New Stack around the latest in developer community growth:
How Community Helps Developers Grow
Empowering Developers Is Critical to Drive AI Innovation
3 Ways Organizations Can Redefine the Developer Experience
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