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**Beyang Liu:** Oh, I think sometimes people say workflow. And that means a certain -- it has a different... There's no single source of authority on what agent or workflow or any of these terms mean, but there is a different connotation, or zone of what that definition means. So I think some more regulatory or legally... |
But long story short is, Cody is still alive and well in the enterprise. It's used for non-agentic workflows. But outside of the enterprise, the reason why we built Amp outside of Cody was really we felt that there was this very big technological shift that was happening. If anything, it's the whole gen AI phenomenon. ... |
\[08:07\] So we didn't want to be hampered by the design constraints and the way that we'd built Cody originally, because that was very much for the chat LLM world. And if anything, a lot of the best practices for building with chat-oriented models, it's the inverse with agentic LLMs. |
**Adam Stacoviak:** Big shift, big waves changing as we have this gen AI world... And we've been three-ish years deep into this phenomenon, as you have said, right? About three and a half years? |
**Beyang Liu:** Yeah. |
**Adam Stacoviak:** It feels a decade. I don't know about you, but I feel it's been a decade. |
**Beyang Liu:** Yeah, it's wild. |
**Adam Stacoviak:** I'm excited. Are you excited about this change? I mean, I feel I've had peak hype, and then a drop down to reality; then peak hype, then drop back down to reality. And I feel now I'm just sort of peak hype all the time. |
**Beyang Liu:** Yeah. I mean, I'm definitely excited about the technology. I think it's got huge potential. I would say I was never excited to the point where I believed in the sort of AGI myth, or this notion that it would eliminate all need for work, or kill us all... I thought that was always a fairy tale told by pe... |
**Adam Stacoviak:** Yeah. I feel it's obviously a force multiplier. I still feel it's this weird genie in a bottle, where you have to conjure it in certain ways, you have to hold it delicately... I feel even in my own experience it's smart in some cases, and then really dumb in some cases... I have some questions for y... |
**Beyang Liu:** Yeah, so the committing to Git, I think that's -- at least for Amp, that's largely solved now. It's been a while since I've seen a class of error -- |
**Adam Stacoviak:** I'm not blaming Amp now. I'm blaming somebody else. |
**Beyang Liu:** Okay, gotcha. I think those sorts of things, I have very high confidence that it could just do now. But I get what you're saying. There is this spectrum where some things that you, a human, would consider difficult, it can just -- you know, almost one shot. Whereas other things where a human -- it'd be ... |
So you can almost tell from talking to a person how much they've used these tools in terms of what they express their frustrations as. A lot of newbie users, especially the ones that are from the onset skeptical for whatever reason, they will say "Oh, I asked it to do this thing that ought to be very easy, but it fell ... |
**Adam Stacoviak:** \[12:26\] Yeah, what did you expect...? |
**Beyang Liu:** It's not a mind reader. There's almost like an information -- you put a certain amount of bits in, and from that the model has to tease out what your intention was. |
It can certainly accept instructions that are like five words long, but if it's only five words, then it's going to be very much based on what its prior behavior wants to do. So if you have a five-word prompt to create a simple React app, or a simple game or whatnot, it could probably do that, but it's not going to be ... |
Whereas the more expert users, they still have complaints, but it's oftentimes around very specific things. It's like, okay, I know what it's good at, I'm not complaining about the fact that it can't do these things anymore... I can still do those things, it's fine. It's more around "Hey, I would love to be getting mor... |
I think one very important bottleneck that still remains is code review. I think most expert users of coding agents realize that they're very powerful, and they want to be using them heavily, but you can't trust them fully. You've still got to read through the code that they emit and understand it, because otherwise th... |
So this process of the human understanding the code that's been generated and ensuring that it doesn't do anything very incorrect or not according to their intentions - that's an important part of the process, and it's quickly becoming one of the key bottlenecks in this process among the folks that are really, really t... |
**Adam Stacoviak:** Yeah. In my case in particular - I'll call out the tool... It was not Amp, just so you know. It was Claude. And it was around 11.30 p.m., 11.45 p.m. Central Standard Time, literally last night. And the example was -- I'm obviously using Claude Code... Well, not obviously. I'm using Claude Code on th... |
In my case, I think it may have been a recent context window clearing... Either way, I just felt like simple tasks were getting harder and harder for it to do. It was almost like it just got unsmart. I don't want to say the opposite word because it's not cool to do that, but it had become unsmart, basically. |
And this specific example was "SSH into this known machine we're working with." Like, it has a name in my hierarchy; it's clear. There's context for what this machine is, how we've been interacting with this machine, and how it would work. And I said "SSH into this machine and just check on the memory state", because t... |
\[16:10\] And so that's the very specific example, versus just this -- I feel like sometimes there's drift in its ability... And that could be a Claude thing, it could be maybe it was a certain time and usage was peaked, and maybe these models get less smart whenever there's peak usage, because there's maybe less memor... |
**Beyang Liu:** Yeah, it's interesting. I don't know when this is going out, but there was a recent report that Anthropic posted, where they were -- apparently, they had rolled out a quantized version of the model over the past couple of days, which actually did yield degraded quality. And so this has kind of confirmed... |
In our case -- we do use the Claude family models heavily underneath the hood, but we have a couple levers that we can pull, that can help address this issue. One is we actually use multiple inference providers that provide Claude. And there's actually periods of time where one provider will have different uptime chara... |
And then the second lever that's just now coming online is we're starting to play around with more and more additional families of models. So we already make use of a variety of models for specific use cases and capabilities within Amp, and we're also constantly trying out different models as the main agentic driver. A... |
**Adam Stacoviak:** Mm-hm. Let's talk about, I guess, how it works, if you don't mind. You're CTL at Sourcegraph, so you should know these things, or at least the README version. I'm assuming you're super-deep. I don't want to assume a lot of stuff, but I figured your position gives you the ability to go probably as de... |
**Beyang Liu:** Yeah, so I'd say there's no weird rocket science here. I'd say at the core, at a very high level, Amp operates the same way that every other agent does. The way I'd describe it - it's a for loop wrapping an agentic LLM. So you take an LLM like Claude, or GPT-5, or any of the other agentic LLMs that are ... |
\[20:17\] And the tool calls are just text. They describe like "Hey, you told me that you had access to these tools." Maybe it's Grep, maybe it's a read file tool, maybe it's a tool to edit a file... "I want to invoke the tool with these arguments", and then our application logic goes and executes the tool call. |
So given the spec that we get from the model, we execute the tool, we get the response, and then that response gets fed back into the next iteration of the loop. And that just keeps looping until there are no more tool calls, at which point the model generates a final response... And that's usually either the answer to... |
Beyond that layer, there's nuances. There's nuances in terms of what tools we provide the models, the different prompts that we use for the main agent, and then there's also sub-agents... So sub-agents are a special class of tools where you call a tool, but underneath the hood it's just its own nested for loop. So it's... |
So there's a lot of tuning that we do to make sure that all those pieces operate well together, that we can eliminate the dumb class of errors, like "Oh, I can't SSH into this thing", or "I can't read this file." Smooth all those out, because those are big disruptors to the user experience. And then we find pieces to f... |
And then there's this client-server architecture that we have. Part of it always has to be on the server, because the best models are still server-bound right now, because local models have not yet gotten to the point where they're fast enough to run locally... But in addition to that, Amp also has a server-side store ... |
\[23:49\] So that's another big component of the Amp architecture, is we do have the server-side component that stores the thread information so you can share with your teammates, and also you can go back and revisit previous threads in case you're like "Oh, what was that thing that I was learning about a couple days b... |
**Adam Stacoviak:** Is the thread simply kind of a chat history? It's not really context, it's just more what was printed back and forth kind of thing? |
**Beyang Liu:** It includes every single message from the assistant or the user, and all the tool calls and all the tool results. So it's basically the entire interaction. |
**Adam Stacoviak:** Like a transcript. |
**Beyang Liu:** Yeah, it's like a transcript. "Here's what you asked it to do. Here are the tools that it called. Here's the results of those tool calls. It read this file, I read that file, it listed this directory, it made this edit... And then you asked it to do this", and then it's just the entire message history. |
**Adam Stacoviak:** Interesting. So you've got a client server, you obviously have your own CLI, so you can install it... I think you install it via Brew on a Mac. I can't recall if that's how I did it, or if it was via npm. |
**Beyang Liu:** The preferred way is via npm for now. |
**Adam Stacoviak:** Gotcha, okay. So you've got that client architecture, that is your own CLI, which by the way, is just stunning. It's beautiful. I love the way y'all did that. |
**Beyang Liu:** Thank you, yeah. |
**Adam Stacoviak:** I really love the nod to NeoFetch... At least I think it was a nod to NeoFetch, the opening splash screen. I could be wrong. |
**Beyang Liu:** \[laughs\] The orb? |
**Adam Stacoviak:** Well, when you launch -- you know how you launch Amp. So when you first -- for the users who may not, or listeners who may not... I'm calling them users. Y'all are listeners still yet, you're not users yet. \[laughter\] But you know, when you launch Amp, if you've ever used NeoFetch on Linux, when y... |
**Beyang Liu:** To my knowledge no, but I'm also not the one who created that splash page, so I don't know if that was an inspiration. |
**Adam Stacoviak:** Gotcha. |
**Beyang Liu:** Part of what we're trying to do -- I think command line coding agents are all the rage right now, for good reason. I think it's super-versatile. And I think now in part because of AI, people are really pushing the boundaries of what you can do inside a terminal-based UI. So now there's all these great n... |
**Adam Stacoviak:** Htop, maybe. |
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