Why Agentic AI Needs a New Approach to Data Infrastructure

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 platformAI Will Steal Developer Jobs (But Not How You Think)10 New Jobs for Burned-Out Software DevelopersJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   

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 skeletonCloud Realities Are Slowing AI AmbitionsAfter becoming cloud computing’s telemetry standard, OpenTelemetry graduates into the AI infrastructure eraJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 dataThe rise of agentic AI on Kubernetes: unleashing the new infrastructure layer Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 disciplineAgents are rewriting the rules of security. Here’s what engineering needs to know.Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 BusinessAWS launches a local answer to TypeSafe’s Jev decision modelJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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.  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 ComplexityA practical systems engineering guide: Architecting AI-ready infrastructure for the agentic eraThe rise of agentic AI on Kubernetes: unleashing the new infrastructure layerJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 platformYour agent context needs a development lifecycle AWS open-sources an AI agent it says is 45% cheaper than Claude Code and Codex Join our community of newsletter subscribers to stay on top of the news and at the top of your game.