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In this episode, we're joined by Stephen O'Grady, Co-Founder and Principal Analyst at RedMonk, to explore one of the biggest shifts happening in software engineering: AI is making code dramatically cheaper to produce, but everything downstream is becoming the new bottleneck.

We discuss why SaaS isn't dead despite the hype, the explosive rise of MCP, why AI agents are overwhelming developer infrastructure, and what happens when every engineer suddenly has dozens of AI developers working alongside them. Stephen explains how package managers, code reviews, security, governance, and enterprise systems are all struggling to keep pace with AI-generated software.

Along the way, we dive into AI coding tools, MCP adoption, developer productivity, infrastructure scaling, enterprise software, open source, package repositories, governance, and why the hardest problems in software may no longer be writing code—but managing everything that comes after.

RedMonk: https://redmonk.com

Stephen O'Grady: https://www.linkedin.com/in/sogrady
Demetrios: https://www.linkedin.com/in/dpbrinkm

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.

We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data creates entirely new challenges around governance, access control, and risk management.
Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.

Apollo GraphQL: https://www.apollographql.com

Matt DeBergalis: https://www.linkedin.com/in/debergalis
Alex Salkever: https://www.linkedin.com/in/alexsalkever

Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL

In this episode of Agentic Conversations, we're joined by Shaun Smith, software engineer, open source advocate, and contributor at Hugging Face, to explore how AI coding has changed almost overnight.
We dive into reinforcement learning, MCP (Model Context Protocol), Fast Agent, Claude Code, open source AI, and why today's language models have become so capable that many traditional software libraries are becoming "liquefied." Shaun explains how reinforcement learning unlocked long-running autonomous agents, why ideas are becoming more valuable than code, and how developers should think about building software in an era where AI can generate entire applications.
Along the way, we discuss Hugging Face's MCP server, Fast Agent, AI-powered developer tools, multimodal applications, MCP Apps, context windows, coding assistants, Rust, Python, TypeScript, open-weight models, software architecture, and what the future of programming looks like when humans increasingly focus on design instead of implementation.
Shaun Smith: https://www.linkedin.com/in/smithshaunDemetrios: https://www.linkedin.com/in/dpbrinkm
Hugging Face: https://huggingface.co
Timestamps:

00:00 Introduction
01:56 The State of Open Source AI
05:18 Reinforcement Learning Changed Everything
07:50 Fast Agent Explained
10:18 Fast Agent as an MCP Reference Platform
12:20 Building Smarter AI Tools at Hugging Face
15:17 Natural Language Search Instead of APIs
17:46 Why MCP Apps Matter
20:06 The Evolution of MCP Apps
23:05 Building AI-Native User Interfaces
26:12 Context Is the New Programming Language
28:00 The End of Code Libraries
29:50 Why Developers Aren't Writing Code
31:25 AI Changes Software Engineering
33:05 The Future of Open Source AI
35:43 Claude Skills That Save Hours
38:02 Training Models with AI
39:05 Building Your Own AI Tools
40:50 MCP for Consumers, Enterprises, and Developers
43:42 Why Shell Access Makes Agents Smarter
45:18 Secure Agent Workflows
46:08 The Future of AI Interfaces
47:02 Outro

In this episode, we're joined by Ben Morss, Developer Advocate at DeepL, who spent months traveling across North America and Europe teaching developers about MCP, building MCP servers, and helping teams understand how AI agents actually use tools.

We dive into the biggest misconceptions around MCP, why so many developers still misunderstand how it works, and what Ben learned after giving talks and workshops in 10 cities across four countries. Along the way, we explore MCP server design, tool calling, security concerns, translation workflows, developer education, and how DeepL is using MCP to bring high-quality language translation into AI-powered applications.

DeepL: https://www.deepl.com

Ben Morss: https://www.linkedin.com/in/ben-morss-ph-d-15bab15/
Alex Salkever: https://www.linkedin.com/in/alexsalkever

Timestamps:
[00:00] AI and API Integration
[00:41] DeepL at DevSummit
[01:19] MCP Roadshow Origins
[03:47] MCP Hackathon Insights
[07:52] Security in Model Protocols
[10:25] AI Expert vs Noob Queries
[16:08] DeepL vs Frontier LLMs
[18:16] MCP vs REST API
[21:39] MCP Servers and DeepL