Channel
Interviewed Person
Malte Ubl
In this conversation with *Malte Ubl,* CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: *AI SDK 6.0 & Agent Architecture* * *Agent Abstraction Philosophy:* AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? * *Human-in-the-Loop at Scale:* The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? * *Type Safety Across Models:* AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? *Workflow Development Kit (WDK)* * *Durability as Code:* The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? * *Infrastructure Provisioning:* Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? *Vercel Agent (beta)* * *Code Review Validation: *The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? * *AI Investigations:* Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? *Python Support (For the first time, Vercel now supports Python backends natively.)* *Marketplace & Agent Ecosystem* * *Agent Network Effects:* The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? *"An Agent on Every Desk" Program* * Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent
All right, we are here in the remote studio. Thanks again to F.NG for lending us the space with Malta who is CTO of Versell. Welcome. Hey, how's it going? Glad to be here. Did I get it right? I've actually never pronounced it out loud until like just now. Yeah, that was completely perfect. It rhymes with Google. Ah, okay. Uh, so perfect that you uh worked on search at Google and AM and whiz, which like I think still people don't know enough about whiz. It is like no longer a secret, but yeah, you can't use it. So like unless you work at Google in which case you probably know what it is otherwise uh there's no reason to really know.
Anyway, suffice to say that you are responsible for a lot of the web as it is today. So thank you for spending some time with us. You also obviously now uh building the next web as as we say with uh Verscell and we we we can cover framework defined infrastructure. I I think you probably saw I have a I have a lot of interest in self-provisioning runtimes. we can cover v 0ero but here really this part is is recorded right after you did ship AI which uh we're
trying to sort of recap right for the general uh lens space audience who may not be watching Verscell as closely as uh I do or or you do so like so basically just generally what I guess is your message to the broader AI engineer audience on what Verscell is doing with AI? Yeah, I think the the the super high level view is that what we're really trying to do is like we we're like the biggest fan of the AI engineering movement and we are also fans of you know we're not not just like going super hard on on hype and the big ideas and
and talking about things but like being very concrete about like you know agents are very exciting and you can actually build them right and so like I think our entire conference was about both making that easier right and and discovering the right abstractions as we're kind of figuring out what the what people actually want to do, right? Which is emerging as we speak. Then the way Versell always does these things is by building things ourselves, right? And so that is both in terms of products, so agents that are products that you can
purchase from Versell and stuff that we do basically in our back office to make our own operations more efficient. And so this kind of building of apps lets us ground kind of what we do in in that reality. and then and then you know kind of extract the abstractions that we feel are really helpful to to then put that on the road. And I think the the probably most talked about thing that we shipped at the at the conference was our new workflow development kit which really really is just a way to make
writing uh like workflows like very idiomatic as something that just becomes kind of first class. It's something you do every day. You think about it. you know write 15 design docs just because you want one of them. It's just something you do literally every day. I think like since you your audience also more generally like in the think probably like listening to what people talk about. I think that's a lot of talk about our work development kit but also like more generally like what is what are workflows? What are agents? How are they related? Do you use one or the other? I would actually love to talk about that as well. But like um
obviously like in our in our uh conference basically introduce just like what we hope is by far the easiest way to make your you know make your agents something that is easily embeddible into complex workflows and to make those workflows durable, zoomable, streamable and so forth. Yeah, I mean as as listeners might know I have a long history of workflows um at at Temporo. Um and I think what's weird is a lot of people are discovering this for the first time. You don't really learn about this in CS classes. You
don't really learn about this in like boot camps or anything like that because it's not really a unit of compute and storage that is taught. It's kind of like emergent from you know Uber and like Stripe and and everyone else. I don't know if there's a version of this at Google. I mean there is there's a version of this at every single company that has been doing anything in computer since 1950. Yeah. Like but what's not necessarily the case that it has been abstracted in any way, right? But like when I when I run a you