Is Agentforce the future of enterprise vibe coding? | Salesforce’s Dan Fernandez
Vibe coding is a developer's dream, but in the enterprise, it can be a nightmare of risk and shadow IT. So how do you saddle the 'wild horse' of modern AI development? Dan Fernandez, VP of Product Management, Developer Services at Salesforce, joins the conversation to share the answer: a new category his team is pioneering called Enterprise Vibe Coding. This discussion reveals how to move beyond flashy greenfield AI demos and build for the reality of most enterprises, where the goal is to safely reuse existing systems, not reinvent them from scratch.Dan breaks down the specific guardrails Salesforce has built, from sandboxed environments for safe testing to automated "quality gates" that act as a bouncer for both human and AI-generated code. He shares the powerful lesson that building customer trust through policies like zero data retention is more important than any single feature. He explains why the real work of enterprise AI is more like secure "plumbing"—connecting the hardened systems you already have. This is an essential guide for any leader looking to apply the speed of AI to the complex reality of enterprise software.Get the guide: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Unleash Your Innovation with Agentforce Vibes: Vibe Coding for the EnterpriseLearn more about Salesforce for Developers: developer.salesforce.comSalesforce Extensions for VS Code: VS Code MarketplaceSalesforce Code Builder: Learn more about the zero-install IDEConnect with Dan Fernandez: LinkedInReferenced in today's show:Future of tech leadership survey report 2025 - Riviera PartnersStop Avoiding Politics – Terrible Software Agentic Commerce I built ChatGPT with Minecraft redstone!Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever
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Building the internet’s next infrastructure layer | Cloudflare's Brendan Irvine-Broque
The Model Context Protocol (MCP) is evolving beyond local developer experiments and into the secure, remote infrastructure that will power the next generation of the internet. Brendan Irvine-Broque, Director of Product at Cloudflare, joins us to share a roadmap for this future. He explains how Cloudflare's "customer zero" philosophy of dogfooding their own tools provides a unique perspective on what it takes to scale MCP for production.Brendan makes the case for observability as the ideal starting point for enterprises and lays out the vision for MCP's ultimate destination: a universal protocol for agent-to-agent communication. The conversation explores how remote servers can create a decentralized layer for security and user memory, and what the exciting development of MCP UI means for the future of chat-based applications. This is an essential look at the next wave of agentic systems and the infrastructure required to build it.Check out:Watch Closing the AI gap: Surpassing executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Cloudflare's work with AI: agents.cloudflare.comRead the latest from Cloudflare: The Cloudflare BlogCloudflare's Unique Primitives Mentioned: Durable ObjectsThe MCP UI Project: MCP UI on GitHub (Project by Ido Salomon)Observability Tools Mentioned: Datadog | HoneycombAI Tools Mentioned: Block/Square's "Goose" | CursorConnect with Brendan Irvine-Broque: X @irvinebroque | LinkedInReferenced in today's show:AI Has Won: Google’s DORA Study Shows Universal Dev AdoptionThe Theatre of Pull Requests and Code ReviewAI isn't replacing radiologistsIs it time to look for a new job? And how do I start?Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever
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Making tech literacy irrelevant | Infactory’s Ken Kocienda
What do you learn after spending 15 years at Apple and demoing your work directly to Steve Jobs? Ken Kocienda, Co-founder of Infactory AI and author of Creative Selection, joins us to share the answer. As a former Principal Engineer at Apple who helped create the iPhone keyboard and autocorrect, Ken discusses his incredible journey from a history major to a key figure in building technology used by billions. He explains his core philosophy of bridging the gap between the liberal arts and technology to create meaningful products, and why he believes AI is the next frontier for this mission. (BTW – we sat down with his co-founder Brooke, so if you like this episode be sure to check that one out!)The conversation dives into his disciplined, spec-driven approach to coding with AI and the power of "extractive AI" to unlock hidden value in data. Ken reveals the crucial lesson he learned from Steve Jobs—that "everything is provisional"—and how his "evolutionary design" process is perfectly suited for today's AI challenges. This episode is a deep dive into the timeless principles of design and a powerful argument for why the best technology is so intuitive, it makes technical literacy irrelevant.Check out:Register now: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Infactory AI: infactory.aiConnect with Ken on LinkedInKen's Book: Creative Selection: Inside Apple's Design Process During the Golden Age of Steve JobsReferenced in today's show:MCP is probably the first protocol in tech history with more builders than users… or at least that’s how it feels.Albania appoints world’s first AI-made ministerSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever
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Why enterprise AI lives or dies on applied research | Contextual AI’s Elizabeth Lingg
What does it take to transform a brilliant AI model from a research paper into a product customers can rely on? We're joined by Elizabeth Lingg, Director of Applied Research at Contextual AI (the team behind RAG), to explore the immense challenge of bridging the gap between the lab and the real world. Drawing on her impressive career at Microsoft, Apple, and in the startup scene, Elizabeth details her journey from academic researcher to an industry leader shipping production AI. Elizabeth shares her expert approach to measuring AI impact, emphasizing the need to correlate "inner loop" metrics like accuracy with "outer loop" metrics like customer satisfaction and the crucial "vibe check." Learn why specialized, grounded AI is essential for the enterprise and how using multiple, diverse metrics is the key to avoiding model bias and sycophancy. She provides a framework for how research and engineering teams can collaborate effectively to turn innovative ideas into robust products. Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Contextual AI: Contextual.ai WebsiteFollow Contextual AI on Social Media: LinkedIn | X (formerly Twitter)Connect with Elizabeth: LinkedInReferenced in today's show:Throwing AI at Developers Won’t Fix Their ProblemsWhy language models hallucinatei ran Claude in a loop for three months, and it created a genz programming language called cursedSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever
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Your AI demo is a lie (and how to make it real) | Arcade’s Alex Salazar
AI that talks is easy, but AI that acts securely is where everything breaks down. We're joined by Alex Salazar, CEO of Arcade, to confront the massive and often underestimated gap between a flashy AI demo and a production-ready system. Drawing from his team's own pivot from building agents to building the tools that secure them, he explains why a working demo is only 1% of the journey. Alex breaks down the four "demo killers" that cause most agent projects to fail: inconsistency, security flaws, prohibitive costs, and high latency.Alex reveals the counterintuitive solution his team discovered: the key to making non-deterministic AI reliable is to dial up determinism. Learn why giving an AI a constrained set of intention-based tools - like a calculator or a multiple-choice test - dramatically reduces errors and solves critical security challenges that plague open-ended systems. He explains why you can't just wrap existing APIs and must instead build custom, workflow-centric tools for your agents. This is an essential listen for anyone who wants to build AI that doesn't just talk, but acts securely on behalf of your users.Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Arcade: Arcade.devArcade's YouTube Channel: Watch examples and walkthroughs on building agentsConnect with Alex Salazar: LinkedInReferenced in today's show:Google avoids break-up but must share data with rivals Welcoming The Browser Company to Atlassian "~40% of daily code written at Coinbase is AI-generated. I want to get it to >50% by October." on X Crushing JIRA tickets is a party trick, not a path to impact Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever
Dev Interrupted is the go-to podcast for software engineering leadership. Each week, hosts Andrew Zigler, Ben Lloyd Pearson, and Dan Lines sit down with industry experts to explore the strategies, struggles, and stories behind high-performing software teams. Paired with weekly industry news coverage, the conversations dive deep into the real challenges that define excellence in modern tech.
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