The New Stack Podcast

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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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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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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