The New Stack Podcast

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, 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.
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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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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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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