The New Stack Podcast

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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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
After becoming cloud computing’s telemetry standard, OpenTelemetry graduates into the AI infrastructure era
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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 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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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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