The Age of Async Agents — Cognition's Walden Yan & OpenInspect's Cole Murray

Cognition co-founder Walden Yan and OpenInspect's Cole Murray discussed the rise of "async agents" — AI coding tools that operate in the background rather than requiring constant developer oversight — in a podcast released Thursday. The conversation comes as Cognition, which recently announced a $1 billion Series D round, faces a growing wave of companies building their own background agents, from Shopify to Stripe to Ramp. Yan and Murray argued that the industry is moving beyond local agents like Claude Code and Cursor toward agent orchestration, where fleets of AI work independently as teammates rather than tools.

The new AIEWF website is live CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get $2k in credits and free AIE WF tickets One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph https://www.latent.space/p/oai-v-langgraph and Pydantic https://www.latent.space/p/pydantic and Flue https://x.com/FredKSchott/status/2050274923852210397 , and managed agents from Anthropic https://www.anthropic.com/engineering/managed-agents and Gemini https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ and Amazon https://openai.com/index/openai-on-aws/ . There has been a wave of companies building their own background agents from Shopify https://x.com/simonw/status/2053529689122328947 to Stripe https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents to Paradigm https://x.com/matthuang/status/2057500542298136899?s=46 to Razorpay https://x.com/shashank kr/status/2056246734465253859?s=46 , and even Cognition’s friends Ramp https://x.com/zachbruggeman/status/2010728444771074493?s=46 have built their own coding agent with other friend Modal https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal . You’d think Cognition might feel a bit threatened, but they’re not - even after all this, they were way oversubscribed for the $1B Series D https://www.latent.space/p/ainews-cognition-raises-1b-in-26b?utm source=publication-search they just announced: Walden Yan https://www.linkedin.com/in/waldenyan , coiner of context engineering https://cognition.ai/blog/dont-build-multi-agents and Chief Product Officer/Cofounder of Cognition, invited OpenInspect’s Cole Murray https://github.com/ColeMurray/background-agents to talk about why the Devin is in the Details https://swyx.io/cognition . Full conversation live on the pod https://www.youtube.com/watch?v=0fgJPhYcbVk today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren’t good enough yet to vibecode, and people didn’t trust AI enough to let it rip, nobody including early Cognition was sure about the form factors. Now it is obvious: The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor’s tab autocomplete https://cursor.com/help/ai-features/tab are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer’s local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.The second wave was local agents : Claude Code https://www.latent.space/p/claude-code , Windsurf https://www.latent.space/p/windsurf , Cursor’s agents pane: first one and increasingly many terminals all running concurrently.The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development. According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three. As Cursor’s Michael Truell put it in The third era of AI software development https://cursor.com/blog/third-era : Cursor is no longer primarily about writing code. It is about helping developersbuild the factory that creates their software. This factory is made up offleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work. The agent should not sit solely inside the developer’s flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else. In less than a year, the sentiment has shifted from avoiding multi-agent systems : to suggesting approaches that actually work : From coining “context engineering” to building the infrastructure behind Devin’s 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray , creator of OpenInspect , to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection , and why “spec to pull request” is now becoming a real production workflow. We go deep on the architecture of background agents : harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine , why repo setup is still one of the hardest problems , why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration , AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer. And as agents eat software… and software eats the world… https://www.youtube.com/watch?v=zepu8Kk6FBQ you can draw the conclusion on what is next: We discuss: Why the engineering world is waking up to background agents and cloud agents The December 2025 model inflection that made spec-to-PR workflows practicalDevin’s 7x merged PR growth and rise from 16% to 80% of commitsWhy Cole built OpenInspect as an open-source background-agent systemThe economics of $20/seat agent products and why monetization is trickyWhat Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption Harness in the box vs out of the box , and why architecture mattersWhy Devin separates the brain from the machine for security and permissions Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments Why full VMs matter when agents need to run real applications and test themAndroid, macOS, Windows, nested virtualization, and machine-specific agent work Why testing is much harder than “computer use” Screenshots, video verification, and the “I know it works” merge moment GitHub UX, Devin Review, AI reviewers, and agents responding to PR commentsWhy MCP alone is not enough for first-class Slack and enterprise integrationsMemory, Knowledge, skills, Claude.md, and why retrieval is still unsolved Devin’s auto-generated memories and the challenge of memory pruning Always-on agents as permanent PMs for issues, tickets, and product areasSub-agents, meta-Devin management, and what multi-agent systems actually add Why pure auto-merge vibe coding breaks down after about two weeks AI code smells, lint rules, reward hacking, and Semgrep for agent-written code GitAI, inline context, and preserving the “why” behind code changes Local testing, mock servers, older codebases, and preparing companies for agents Windsurf 2.0 and the handoff between local foreground agents and cloud background agentsSRE auto-triage, support workflows, and agents as first responders PMs, marketing, and non-engineers creating pull requests from Slack AI agent budgets , $1k-$5k per engineer spend , and hybrid frontier/sub-frontier systemsThe rise of autonomous coding factories and who Cognition is hiring Walden Yan Cole Murray LinkedIn: https://www.linkedin.com/in/colemurray/ https://www.linkedin.com/in/colemurray/ OpenInspect / Background Agents: https://github.com/ColeMurray/background-agents https://github.com/ColeMurray/background-agents Timestamps 00:00:00 Introduction 00:00:43 Why Everyone Is Building Their Own Devin 00:01:57 Devin’s 2025 Ramp: 7x PR Growth and 80% of Commits 00:03:49 OpenInspect and the Rise of Open-Source Background Agents 00:07:59 What Cognition Actually Sells Beyond Devin 00:09:56 Background Agent Architecture: Harness In vs Out of the Box 00:12:08 Separating the Brain from the Machine 00:14:07 Repo Setup, Secrets, Docker, and Full VMs 00:19:13 Why Testing Is Harder Than Computer Use 00:22:40 Video Verification and the “I Know It Works” Merge Moment 00:23:19 GitHub UX, Devin Review, and AI Code Review 00:25:42 MCP, Slack, and Enterprise Agent Integrations 00:28:59 Memory, Knowledge, and Always-On Agents 00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin 00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay 00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore 00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems 00:56:10 Making Codebases Agent-Ready 00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff 01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases 01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories 01:06:51 Hiring at Cognition and OpenInspect Consulting 01:07:45 Outro Transcript Introduction: Walden Yan, Cole Murray, and Context Engineering Swyx 00:00:00 : All right, we’re in the studio with Walden Yan, co-founder of Cognition, CPO. Walden 00:00:08 : Happy to be here. Swyx 00:00:09 : Which is a cool title. And coiner of context engineering. Walden 00:00:15 : Although I think there are many people who’d used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents. Swyx 00:00:33 : For those who haven’t caught up on that, I have on screen the Don’t Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect. Cole 00:00:43 : Great to be here. Swyx 00:00:43 : So let’s talk about it. Everyone is building their own Devins. What’s going on? The December Shift: From Handholding Models to Autonomous PRs Cole 00:00:51 : So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you’d like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical. Swyx 00:01:41 : I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side. Walden 00:02:01 : In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it’s been ramping up even faster. So it’s almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That’s a more serious conversation, and we’ve seen that in all of our growth charts. Internally there’s this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called. Swyx 00:02:57 : I think Dev, maybe tweeted that. Yes. Walden 00:03:01 : it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It’s, gone up by, 10% or something. Swyx 00:03:11 : We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March. Walden 00:03:25 : It’s a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there’s Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it’s good to hear, what initially inspired you to try to build OpenInspect? OpenInspect: Ramp, Cloud Agents, and Open Source Cole 00:03:49 : OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI’s Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can’t see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there’s nothing to jump in on unless they’re copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there’s actually a thread of where I live tweeted, going through this Cole 00:05:14 : comparing GPT and Claude as both of them are going through it. Swyx 00:05:17 : On the announcement thing or something else? Cole 00:05:19 : right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here’s how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn’t really see anything in the open source community that, met this type of system. I think there’s a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of. The Business of Background Agents: Open Source vs. Devin Swyx 00:06:16 : So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don’t know if you tried that or Walden 00:06:22 : I was going to say, one of the things that interested me a lot with OpenInspect was, you didn’t try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise V Swyx 00:06:36 : That’s why no OpenDevin. Yeah. Walden 00:06:38 : yeah, and how did you think about that? I thought that was very interesting. Cole 00:06:44 : I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?” Walden 00:07:08 : I’m sure you’ve gotten offers. Cole 00:07:09 : but primarily I don’t want to do that for a few reasons. One, I think that I don’t want to compete for, $20 a seat. I think that is just a really difficult business. I think it’s very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it’s hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You’re selling, I guess, the infrastructure. You’re selling, the integrations maybe. Swyx 00:07:55 : let’s ask the guy. What are you What are you selling? Walden 00:07:59 : Well, yeah, there’s multiple layers to this in practice, and actually it’s funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because, Swyx 00:08:10 : You had to build this two years before everyone else,? Swyx 00:08:15 : Including, the model side Walden 00:08:17 : It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that’s just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don’t have to worry about all the compute side of things. We’ll make it work. We’ll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well. Swyx 00:09:56 : So let’s talk about, architectural stuff. I think that’s always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I’ll just leave the floor open to you guys. Agent Architecture: Harness in the Box vs. Out of the Box Cole 00:10:11 : I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make. Swyx 00:10:22 : But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there’s, that’s the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out? Cole 00:10:39 : In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you’re making some trade-offs by doing that. The negative trade-off you’re making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you’re making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you’re running it in the box, all of the state of that agent is actually in the box, and yes, it’s you could persist it elsewhere, but it’s all localized and you have less concerns to worry about. Walden 00:12:08 : I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don’t have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we’ve seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there’s no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it’s hard to do the separation. But in practice, with Devin, it’s much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don’t have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine. Swyx 00:13:31 : I was going to just bring, I have this, chart from OpenAI, where I don’t know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agents Swyx 00:13:44 : Which is, this is their thing. I don’t know. It’s all, it’s all variations of the same pattern, right? Cole 00:13:49 : So this would be out of the box. Swyx 00:13:51 : Which, is preferable for them because it’s less work? Cole 00:13:56 : I would say it’s more work. Swyx 00:13:58 : It’s more work? Cole 00:13:58 : But it, in my opinion, it is the better architecture of the two. It’s just, you’re taking on a bit of complexity by doing that. Repo Setup, Docker, and VM-Based Development Environments Walden 00:14:07 : One thing I’ve not seen a lot of other players do well is how do you manage what’s actually on the box? And this can be complex for many reasons. Let’s say you have a big repository that’s changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let’s say, run the app and test it, and all the things you want your autonomous Swyx 00:14:34 : So a repo setup. Walden 00:14:35 : Exactly. So in, internally At Cognition, we call this repo setup. Cole 00:14:39 : The hardest part of Walden 00:14:40 : It’s been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem with Swyx 00:14:53 : How do you solve it? Walden 00:14:53 : Your clients? Cole 00:14:54 : This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don’t really have great developer environment setups, if any. A lot of the times it’s, “Go talk to Bob and get the secrets,” and that obviously doesn’t work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for the Swyx 00:15:19 : Even in prod? Cole 00:15:20 : Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system. Walden 00:15:48 : And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There’s lots of reasons, I think, why Docker containers are not great. One thing is, Docker container’s not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there’s actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays? Cole 00:16:57 : I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they’re not, then I don’t know what they’re using. But they’re probably already using Docker Compose. Swyx 00:17:14 : I’ve always had a small candle for web containers. I don’t know if you guys have tried them before. Swyx 00:17:19 : To me, they were, supposed to be like Docker Light. Cole 00:17:22 : Is it? Swyx 00:17:22 : I don’t know. Cole 00:17:22 : No, I haven’t tried it. But yeah, I think any environment that you’ve set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you’ve more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox. Testing Agents: Computer Use, Screenshots, and Real App Workflows Walden 00:18:08 : Another thing that we’ve been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specific Walden 00:18:20 : VMs? Walden 00:18:22 : There are like many technologies in the world that only work on specific types of machines, right? If you’re building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that work Swyx 00:18:32 : Does Commission support Swyx 00:18:33 : Choices like that? Walden 00:18:35 : The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we’ve actually recently added support now for, it’s in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there’s like weird performance issues that like, it, which is why it’s like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development. Swyx 00:19:13 : I was trying to find like a reference video for the testing thing. I couldn’t find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don’t know, it’s, what’s the general Category of problem? Walden 00:19:26 : I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting like Walden 00:19:48 : Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we’ve specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself. Walden 00:20:42 : We’ve seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it’s worth has gotten a lot better with recent models and it’s made that part of the job certainly easier. Swyx 00:20:58 : Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use. Walden 00:21:08 : Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don’t regress? Swyx 00:21:25 : Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor’s recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while. Swyx 00:21:57 : Oh, yeah. See how screenshots work. Yeah, I don’t know if there’s anything, super and not obvious. It’s like once what feature to build, you can just prompt it and it Will mostly work. Walden 00:22:09 : I think to Walden’s point, though, the computer use is a subset of the larger testing problem, and I think that’s very specific to the code base that you’re working and it’s not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model. Swyx 00:22:40 : For those who haven’t seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It’s very small here, but it actually labels what it’s testing. And then it-- and then you actually see the cursor and everything. So I don’t know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don’t I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don’t need to see the code. I know it works. Walden 00:23:19 : Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those. Swyx 00:23:27 : It’s just like, what am I looking at? What are you trying to demonstrate? Walden 00:23:30 : Exactly. There’s a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you’ll think about, oh, it’ll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there’s actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then go GitHub Workflows: Devin Review, Comments, and PR Automation Swyx 00:24:23 : He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it’s, that I have commented But usually it’s just me saying like, “Hey, merged, fix any merge conflicts.” Walden 00:24:37 : The, so when Devin fixes his own comments, you might be scared that, oh, maybe I’ll infinite loop. But we’ve put a lot of work into making sure it doesn’t, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it’s like, “Wait a second, I think you’re wrong.” Actually, that’s one of my favorite moments is when Devin tells me that I’m wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is. Cole 00:25:06 : I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn’t do them automatically yet. The capability is there, but it’s not fully used. Swyx 00:25:27 : So you have to ask for it? Cole 00:25:28 : you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn’t do it automatically yet. Integrations: Slack, MCP, and First-Party Agent Interfaces Walden 00:25:42 : Well, I’m curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it? Cole 00:25:52 : I think a lot of it comes down to actually integrating it into the company. It’s one thing to have the background agent system set up, but if it isn’t actually integrated into your larger ecosystem, it isn’t that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they’re in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this. Walden 00:26:46 : The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that’s how it’s been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don’t spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there’s a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places. Swyx 00:27:39 : I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that the Walden 00:27:48 : I would love a world where you could have something that’s more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces. Swyx 00:28:03 : So there actually is sampling in the MCP spec, but nobody Uses it, right? Walden 00:28:07 : And so I think that’s the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now. Cole 00:28:29 : I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf. Swyx 00:28:43 : Awesome. Other than MCPs, what else, sorry, well, I don’t know if that’s Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on? Memory and Knowledge: What Agents Should Remember Cole 00:28:59 : I think, a problem that comes up very frequently is this idea of memories or knowledge base. Swyx 00:29:05 : Oh, boy. How do you solve it? Cole 00:29:08 : so not solved yet, is the short answer. Cole 00:29:11 : it’s something, there’s a open issue for it, someone asking about it. Swyx 00:29:16 : There’s, I, D Wiki hasn’t indexed anything about memory yet. Cole 00:29:20 : how I’m seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I’ve been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it’s a very difficult retrieval problem. Swyx 00:29:44 : Oh my God. RAMP didn’t write anything about memory? I see zero search results. Walden 00:29:50 : No. Memory can be quite tricky to get right because it’s the retrieval, but also the generation of the memories that can be really tricky. You don’t want it to just like Remember very specific details. Swyx 00:29:59 : Walk us through the Devin memory journey because I know there’s been a journey. Walden 00:30:03 : the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that’s not quite the way you’re supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here’s how you’re supposed to work with the technology, et cetera. The generation and the retrieval has been something that we’ve been trying to tune a lot over the years. Generation, you don’t want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don’t want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they’re retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go. Cole 00:31:31 : Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory? Swyx 00:31:36 : Deleting and forgetting? Walden 00:31:39 : The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don’t do that.” And then it’ll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don’t know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we’ll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That’s been an interesting exploration. Also similar ideas in the scale space. Swyx 00:32:35 : I am pulling up OpenClaude’s memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don’t know if it’s the best. It’s probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don’t get recalled again or something. I don’t know. Walden 00:33:01 : One thing we’ve been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it’s just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they’ll even Devin will even tag you on some recurring basis. And so it’s been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it’s just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do. Swyx 00:34:00 : One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That’s the automation. I can’t find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you’ve suggested that I don’t want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request. Walden 00:34:31 : what? We might change it soon. I guess OpenInspect, in the time you’ve been around, has there been anything you tried to implement but then you had to like undo and like do a different way? OpenInspect Architecture: Webhooks, Control Planes, and Agent State Cole 00:34:41 : Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what’s handling the webhooks, and then is basically interacting with the control plane. As I’m seeing the system starting to be more integrated, specifically with the GitHub bot integration, I’m considering bringing that all into the central control plane because especially now I want to start, And a request that I’m getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking of Swyx 00:35:19 : What do I have open? Cole 00:35:21 : What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it’s a question of do I put that webhook in that GitHub bot package? That’s weird. It doesn’t really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that’s something that’s on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I’ve added are calling back into the control plane so that you don’t have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it’s fine. Subagents and Multi-Agent Systems: When Parallelism Helps or Hurts Swyx 00:36:16 : Just, a quick question on pulling the agent out of the box. I’m One thing I’m very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can’t tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you’re, it’s less easy because you are, you have a unicorn pet of a, of a harness that’s, living outside the box. Cole 00:36:45 : In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that’s running. And so that session is also running outside of the box. It’s running in your worker plane, wherever you’re running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it’s probably fine. Swyx 00:37:26 : Well, then I’m just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin’s calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything? Cole 00:37:46 : I think one of the surprising things we’ve seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We’ve actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we’ve still found that most practical use on a day-to-day basis has been one single Devin. Cole 00:38:33 : Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there’s, a very little room for conflict is the regime that you have to create today. Swyx 00:38:50 : I’ll call out, the experiments from Cursor, right? This is Wilson Lin’s work on Single agent to multi-agent, and you’re obviously famously on the side of don’t build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think. Cole 00:39:08 : I think there will be a revision to that post at some point About Swyx 00:39:12 : Tell us about it Cole 00:39:12 : I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There’s a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It’s not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I’m wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They’re not just, yes men. Claude, I guess is like, used to just say, what is it? “You’re right,” or, Swyx 00:40:25 : “You’re absolutely right.” Cole 00:40:26 : “You’re absolutely right.” Yeah. Swyx 00:40:28 : The Have you seen, did you see Cole 00:40:29 : The age is over Swyx 00:40:30 : The Codex app troll in Topic? This is the Codex app. Inside of Settings, there’s a little, there’s a little Easter egg, right? So if you go to, the Themes or Appearance, right? There’s all these, color codes, and the top is absolutely, and it’s the Topic’s colors. Which is such a troll. Anyway. Model Behavior: Pushback, Adversarial Prompts, and Agent Skepticism Cole 00:40:53 : I love that Easter egg. Did you discover that yourself? Swyx 00:40:54 : No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors. Cole 00:41:06 : Yeah. So yeah, we’re out of this regime where, it just says you’re absolutely right, and they can have real conversations and real back and forths. Swyx 00:41:13 : You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that’s, a dumb tool, it’s actually pushing back on you I think. Yeah. Cole 00:41:24 : when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser. Swyx 00:41:34 : That was I think that was the one. Cole 00:41:36 : You can have, like Swyx 00:41:37 : I think it’s the same one Cole 00:41:37 : Creation of it. We found a surprising success of, don’t do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It’s Swyx 00:41:55 : Was this Andrew’s thing? Cole 00:41:58 : there were lots of demos that we ended up not posting, ‘cause at some point we’d just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it’s absolutely the future. I think we’re heading in that direction and we can see the progress being made there already. Swyx 00:42:25 : If I were to, make one super minor pushback because I don’t feel that confident about it yet Cole 00:42:33 : Go for it Swyx 00:42:33 : But I’ve had Ryan Lopopolo from OpenAI on the pod And he’s a super slop cannon, right? Oh my God, that’s my coding agent being done. I downloaded this, Peon Ping. I don’t know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it’s done. And so it’s like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan’s thing was that there’s a slop cannon approach where you try to loosen the single agent’s, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don’t think anyone’s, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn’t solved it, but he thinks he’s He thinks he’s completely solved it. But I think it’s still I think it’s, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I’m like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don’t know if that’s, feasible at some point in the future. Code Review, Entropy, and AI Slop Walden 00:43:55 : I And we’ve also run experiments internally where we’ve basically tried to build entire products, true products that we knew we would eventually ship, but for now, let’s try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there’s this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.” Walden 00:44:18 : “Let’s actually rewrite it from scratch.” Swyx 00:44:19 : Start a new factory, yeah. What’d you find? Walden 00:44:21 : I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you’d find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it’s a slightly different color in one spot. And you’re like, “Okay, this is too much to work with. Let’s actually try to do code review at the same time.” And make sure that we’re on top of our software, actually cleaning it up a bit And making sure it’s done in a scalable way. Cole 00:44:54 : I think building on that, the idea of, you don’t have to look at code, I think is generally a bad idea. And the meme that I have for that Walden 00:45:03 : What timeline, all right, is Do you think that statement will be true on? Cole 00:45:06 : I think probably for a while it’ll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You’ll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl. Swyx 00:46:09 : Within balance, I think it’s fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I’ve been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it’s your job as an architect, as a CTO, whatever, to say like, “Okay, here’s the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let’s be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that’s that. I don’t know if you have any other modifications or advice. Walden 00:46:44 : Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I’ve actually seen this come into play when actually building agents. So we’ve had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents’ machines.” And so a lot of them, I assume because they’re using AI and they themselves don’t have, super deep infra background knowledge, say, “Okay, we’re going to go build our own custom Grep index. It’s going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn’t have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so. Infrastructure Details: Grep, File Systems, and Sandboxes Swyx 00:47:45 : What do you mean you hand-coded Devin? What? Walden 00:47:48 : It’s like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they’re like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don’t use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you’re Grepping, you’re actually making network calls Every time you’re doing these things, and that’s why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. But Swyx 00:48:35 : I think there’s a write-up about it, right? Silas did one about the virtual file system. Walden 00:48:38 : Oh, that was a whole other thing. The Swyx 00:48:39 : Oh, that’s a different thing Walden 00:48:40 : The BlockDev file storage format Swyx 00:48:42 : I’ll bring it up Walden 00:48:42 : Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you’re not optimizing for this case, it’s just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you’re only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there’s a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good. Swyx 00:49:39 : It’s, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra. Walden 00:49:46 : At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don’t need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you? Cloud Providers: Modal, Daytona, and Enterprise Sandboxes Swyx 00:50:12 : Whereas you’re Cloudflare dependent. Cole 00:50:15 : so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there’s an abstraction in place that if any contributor wants to add a new provider, they can add that in. Walden 00:50:32 : Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house? Cole 00:50:44 : most of them I see using Modal. I think Modal has a great Walden 00:50:48 : Shout out Modal. Swyx 00:50:48 : Shout out Modal. Cole 00:50:50 : I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it’s a pretty nice offering as a whole. Swyx 00:51:04 : no debate there. Walden 00:51:07 : Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on. Swyx 00:51:20 : Is there a point So Modal’s very Python, and I feel like most workload, has really shifted to JavaScript. I don’t know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That’s roughly. I think that’s wrong now. I think JS has won. I don’t know if you guys Like, I Maybe I’m overstating it, and maybe for cognition, there’s, C and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter? Cole 00:51:52 : I think that most of the libraries that I see in this space are Python native first, especially in the Cole 00:51:58 : Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice. Swyx 00:52:11 : That’s my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It’s just, one extra step That, isn’t native to the runtime. I don’t know if Walden 00:52:22 : I don’t know Swyx 00:52:23 : Reviews. Do you have numbers? I don’t know. Walden 00:52:25 : the one thing I don’t like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, and Swyx 00:52:32 : Oh, because it’s, mixing two and three or what? Walden 00:52:34 : I think it’s something mixing two and three, yeah. The I don’t know if you see this. It always tries to do, has attribute on objects as like Cole 00:52:41 : Oh, my God. Walden 00:52:41 : But it’s like But that you shouldn’t be doing that. It should error if there was Swyx 00:52:45 : Because it’s training on library code? Cole 00:52:47 : I think it’s more of, like Cole 00:52:48 : From what I’ve seen, it’s more of, a reward hacking mechanism where it doesn’t want to basically Walden 00:52:54 : It’ll never error. Cole 00:52:54 : It doesn’t want the code to fail. And so it Even when it knows it has the attribute, it’ll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we’ve put that in as a lint rule That if you do getattr, your pull request is going to fail. Slop Signatures: Comments, Backwards Compatibility, and Types Swyx 00:53:12 : Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in? Walden 00:53:21 : So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it’ll, comment every line, but it’ll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren’t slop, descriptions like they were before. “Oh, here’s what this function does.” It’s like, “Oh, here’s actually the reasoning and why we chose this approach and what the alternatives were and why we shouldn’t do those alternatives.” Still too much information, but I wonder if this actually might be directionally correct if you want systems that can self-maintain themselves in the long run. Swyx 00:54:04 : Oh, they write the specs inline. Walden 00:54:05 : Have all the context In the code as well. Yeah. Swyx 00:54:07 : So you approve? Walden 00:54:09 : I But at the same time, it’s this tricky problem. Maybe we’ll just give our users, a setting or something, for, how verbose you want it to be. I haven’t loved it. Honestly, I just I like the comment, but please, get rid of it. But I could, I could see a world where maybe something of the sort becomes reality. I don’t know If you guys know about GitAI. So Swyx 00:54:32 : We’ve talked about it, yeah. Walden 00:54:33 : GitAI, the idea behind it is Swyx 00:54:34 : I’ll bring it up Walden 00:54:35 : That if you run an agent, the actual prompts you send to the agent should be stored alongside the code inside the Git metadata so that future agents can reference it, maybe code review bots can reference it. And it’s ideal world where, your context for why decisions were made constantly lives aside, beside your code. And so it’s, maybe a more hidden version of this, write massive PRDs for every comment approach. Swyx 00:55:01 : I’m waiting for the real bull case where we just get rid of Git altogether. We’re not I’m not, I’m not there yet, but I’m looking for it because that would be a big shift. Cole 00:55:11 : On the topic of, visible slop, a pattern that I see a lot of across GPT models specifically is backwards compatibility, at all costs Cole 00:55:21 : Where it’s doing these weird import exports so that it doesn’t have to modify, the names of where the modules were. And I’ve seen Claude 4.6 starting to do this as well. Cole 00:55:33 : And again, I think it is this, reward hacking behavior where it doesn’t want failure to occur, and you can address that through, Semgrep or other tools where that behavior is pretty easy to identify. But it’s something that you only learn through the trade of just seeing code patterns. Untyped tuples are a really big problem of just, again, just throw any in there, dict string any. And again, you can address those through linting. Local Testing, Mock Servers, and AI-Ready Codebases Swyx 00:56:01 : Awesome. Yeah. Any other So, linting, any other tools? Devin Review, of course. Not so, not so free now, but still use it. Walden 00:56:10 : Well, the one thing that I think we try to recommend teams as they use more AI agents, it goes back to this, local testing thing. In the end of the day, you want your agent to be able to do the full thing, not just write the code, but actually run it and test it. And a lot of code bases were not necessarily built for this from the start. For example, you probably do want a local DB setup, a local Docker Compose and Postgres in order to have it so that you don’t need to give your agent any crazy product credentials to actually run and test its code. We’ve also internally done a big shift to make a lot of our core, components of code testable as purely local dev without needing to actually, integrate with, any live services for this reason. And honestly, the older the company, the more you have to change to shift in this direction. But you can use AI to help you perform this migration nowadays. Swyx 00:57:02 : The older, the older the company, the more you have to change in order to do local dev? Walden 00:57:05 : I think so. Swyx 00:57:06 : Or am I misunderstanding? So you’re saying Walden 00:57:08 : Or often times Swyx 00:57:08 : Most people just build with full integration to all their stuff, and there’s no code path to switch it to local. Walden 00:57:14 : Especially in, when there’s, lots of different services and you have, microservice architecture, making that shift, the larger the code base, the harder it is. I guess if you did build it correctly from the very start, I think it’d be possible. But also, a lot There are a lot of companies in the world that got started before Docker was a thing, and so You’re forced to make a migration at some point. Swyx 00:57:35 : Well, Devin’s good, very good at making mock servers. Right? So, And no, the Well, one of the projects that I really want to It’s like, it’s like Little Snitch. I don’t know if you guys have heard of this. Cole 00:57:44 : I run Little Snitch on my computer. Swyx 00:57:46 : It’s just like There’s, a man in the middle, but it, shows you all the traffic going back and forth. But then from there you can reconstruct the server, right? And then, and then, create local mocks so you can local mock everything if you just observe traffic for a little bit. Cole 00:57:58 : That’s an interesting idea. Swyx 00:58:01 : cool. I don’t know if this will get anywhere, but I wanted to maybe talk a little bit about the CloudCode, leak because usually if I have an Anthropic person on, I can’t talk about the CloudCode leak. Did you guys learn anything from CloudCode? I Walden 00:58:19 : So if I say Cole 00:58:19 : This is the first time I’ve seen it Walden 00:58:19 : I was not that, interested in the Leak. We didn’t spend that much time on it Walden 00:58:24 : If I was to say, but Swyx 00:58:25 : I’m just, I’m just, fishing for Cole 00:58:28 : no, I didn’t really, Cole 00:58:29 : Research too much into it. Windsurf, Local Agents, and Cloud Agents Swyx 00:58:30 : Fair enough. Okay, one more last thing before we go. Windsurf 2.0, you guys shipped another thing. So The meta context is you use background agents enough, sometimes you’re going to want to bring them to foreground. And that little, hands-off from local to cloud is hard to work on. And then And Devin has Or Cognition has just done it. Walden 00:58:50 : I think for me the biggest, gap this is trying to close is, again, how do you make the testing process as fast as possible? When it can test on its own and send you a video, it’s freaking magical. Sometimes there are just really difficult things you can that you do just need to, pull down locally. And we just want Windsurf to just be your, local command center of all your agents, your background ones, your local ones, and you can imagine, “Oh, okay, this agent needs me to review something. I’ll pull that down, move my other agents to the background, go test it. Okay, boom, done. On to the next one,” right? You have some issue you got to fix in the background, just click, approve. Okay, set up, start a background agent to go fix it. I’d love a world where I don’t have to leave this window. Then maybe the other window I got to figure out how to stop spending so much time into Slack, but maybe, someday We’ll want to get those tools all. Swyx 00:59:38 : And does that require the binaries to be exactly the same for local versus cloud? Walden 00:59:46 : So the funny thing here is that the behavior between local agents and cloud agents, I think is actually a bit different In their ideal state. I think local agents, you want them to be a bit more fast and let the user make the call on things. Actually don’t try to autonomously go test things. The background agent mode where you go start it off, I think the agent should just assume the next message I send a user should just have everything that the user needs from me and not run and stop Keep running and don’t stop until you have the testing Until you have full report. Swyx 01:00:19 : So that’s a, that’s just a slightly different prompt. Walden 01:00:20 : But for many reasons, because of all the work we do to make sure that Devin works with different Git providers, that it works with different, OS’s and VM’s, we want as much of that logic to be shared as possible. So for our own practical purposes, we try to share as much of it as possible. Swyx 01:00:36 : Yeah. I mean, I can’t imagine how much work it is to, transition back and forth, so congrats on shipping this. Swyx 01:00:45 : okay. Anything else that we should cover before we, wrap? Just whatever you guys were talking about in your lunch. Walden 01:00:52 : maybe, use cases. What are your, do you find to be, the biggest things that your clients are trying to do with their cloud agents today? Cole 01:00:59 : Do you want to just ask it again so we can get, a clean cut? Swyx 01:01:02 : Because he was drinking his water. Yeah. Walden 01:01:04 : The thing I wanted to talk about was use cases. What do you think are the main things that your clients come to you today about, “Hey, this is why we want to go set up cloud agents”? Cole 01:01:15 : I think the easiest and most common use case I see across everyone is SRE use cases. The idea that whether we have our alerts in Slack or Datadog or wherever they’re going, we want the agent to be the first responder on that. And that doesn’t necessarily mean that the agent is actually resolving the issue, but just being able to collect that context ahead of time is huge. Because again, that agent is integrated into the production logs, the database. It has full visibility, and over time, playbooks as well for how to address certain issues. And so that’s a huge win for teams because instantly you can have a full trajectory of what is going on within the system, and oftentimes actually a pull request directly from that, which is a pretty neat flow to actually experience of, error pull request done. OpenInspect does support a trigger for that as well, so that could happen completely autonomously. Swyx 01:02:09 : From Datadog specifically, or just Use Cases: PMs, Support, Security, and SRE Cole 01:02:11 : it supports Sentry, it supports a generic webhook, and if someone wants to add Datadog, they can. The other use cases that I see, are for non-builder use cases, whether that’s the PM or the marketing team. I’m seeing a lot of, teams where the idea of who’s actually contributing code is starting to change. And in a lot of cases, the PM, if there’s just a quick bug fix, the PM is not creating an issue anymore. The PM is just prompting through Slack, and the pull request is then being created. And so I think that’s a huge win. I think that trend will continue, where we’re seeing, code modifications happening outside of engineering. The last common use case that I see is customer support. And so where they’re experiencing an issue with a customer, they’re not entirely sure why this behavior is happening. Previously that world was, “Hey, there’s a bug when they tried to use this feature. We don’t know what’s going on.” Well, they’re now tagging that in Slack. Again, that entire full context is ready. They can then just tag in engineering and have a complete understanding of that issue and completely bypass the previous pain points of like, “Oh, can you get more information from them?” Walden 01:03:24 : The only things I’d add on top of that I think I’ve seen is, continual security scanning Continual security review Is a very big one as well. The SRE use case, internally we think about it as auto triage Because we just want every message that comes in, and that’s an alert, that’s a bug report, to have Devin just start triaging it before anything else. And we’ve leaned into this use case so much though that we’ve basically tried to make it so that you don’t ever have to leave Slack to interact with this. So again, making the interactions with Devin super fluid from the moment the report comes in to it responds to a report and be able to ask it questions right there with full code-based context about all the issues. Very related to customer support as well, I think one thing that we found is CLIs can sometimes be, very difficult for people who aren’t technical to go and use. But an online chat interface that anyone can go and ask questions and is super intuitive and doesn’t assume you have any technical knowledge but does have access to all parts of your code base, super useful For support, for salespeople, anyone who might need to have their questions answered about the code base. So yeah, great callout. Swyx 01:04:32 : This might potentially be, a very expensive, use case. Is there like a rule, sense, a rule of thumb on, how much people should spend on this? ‘Cause, you have unlimited budget, but not other people don’t,? I don’t know if this is an answerable question because obviously it depends on, a lot of factors. But I guess, like Cole 01:04:51 : I think it depends really on, how people are using it. I think If people are using it responsibly and they’re getting value from it, then, you can kinda determine the budget. Common numbers that I hear are anywhere from 1,000 an engineer up to 5,000 an engineer. I have not heard anywhere in the realm of, 50,000 an engineer for a frame of reference. Model Costs, Smart Routing, and Frontier Tradeoffs Swyx 01:05:12 : We’ll get there. Walden 01:05:13 : I’ve seen, I’ve seen numbers go that high for sure. I think that this is also I think going to be a big theme of the coming year, is we’re going to see very expensive, very smart frontier models, And we’re also going to see people who say, “ what? I don’t need the frontier anymore for a lot of the work I do,” because some frontier models actually are good enough For a lot of the work. Swyx 01:05:36 : Also shout-out you pioneered Smartfind Which is a mix. Walden 01:05:39 : I’m really interested in a world where you basically have hybrid frontier and subfrontier systems Where you use the subfrontier part to be really fast, really efficient, and call out to the frontier part of the system so that you can still get frontier performance for the most part. Swyx 01:05:54 : I’m trying to search, but Twitter search is, completely broken. I, it’s, the from field is just completely gone. It’s very sad, Because I really want to Walden 01:06:04 : No worries. I might have to make a new post at some point about the return of Smartfind. Swyx 01:06:10 : Anthropic has now officially adopted it. Okay, cool. I think that’s it. It’s really great discussion and good, great having you guys on. Background agents are a thing now, and everyone’s building them. We, but we talked a lot about, the production concerns and like, well, why you would want to offer one architecture over the other. Yeah, lots to look forward to. Walden 01:06:35 : There’s a real zeitgeist in the space right now I think, for companies to want to turn themselves into these autonomous coding factories. And yeah, we’re doing a lot to try to support that. And so, any listeners are welcome to come chat to us about that, whether using Devin or working with us. Wrap-Up: Hiring, Consulting, and Agent Adoption Swyx 01:06:51 : Hiring? Swyx 01:06:53 : what, specifically, just like give like one profile that’s, very interesting. Walden 01:06:58 : I think people underestimate the role of, really high-taste product engineers In this space right now. Swyx 01:07:05 : And the test is, what have you shipped end to end that is A tasteful product. Walden 01:07:10 : If you’ve shipped stuff that you think is tasteful and you’re, and you’re proud of, you should, you should come talk to us. Cole 01:07:15 : For me, any businesses that are looking to further their engineering org, a lot of the consulting I do is around that. Teams who are maybe starting their AI journey, whether that’s with Cursor or Claude Code, but they’re looking for someone to help navigate them through the state-of-the-art and beyond just that initial deployment. As mentioned, there’s a lot of lift from you’ve deployed the background agent to how do we actually get this fully integrated into the company and really realizing the true value of that. Swyx 01:07:45 : Okay. Well, thanks you guys for coming on. Walden 01:07:47 : Thanks for having us.