Very good. Let's kick things off. Hello

everybody. I'm Kevin de Marco over from

the SpaceX AI product team. Very excited

to be presenting GrokBot for product

with my colleague.

>> Yeah, hey. I'm Roshan, also on the

product team at SpaceX AI. I have been

spending a lot of time using GrokBot and

working on it behind the scenes. So

excited to show you guys a little bit

about how we use this everyday.

>> Awesome. And we are joined by Noah, who

will be moderating today's discussion.

Noah, do you want to introduce yourself?

>> Uh yeah, great to meet everybody. My

name's Noah. I'm on our AI deployment

team here.

Throughout the presentation, feel free

to drop questions in the chat.

I'll be answering them as well as like

probably surfacing some topics up to you

guys.

>> Amazing. I wanted to start with a big

change which I think we are probably all

well aware of, which is not only is

product management, the craft of product

management changing, but so too is the

way in which everybody is building

software. And so I really like this DHH

quote off the Lex Friedman podcast

recently.

The developer's developer, I think

really capturing sort of this moment,

which is software is product management.

What should the product do? Who should

it do it for? How should it do it? How

should it look? And what are the

priorities? Like these are all questions

that now everybody is capable of

wrestling with as we start to build

products and software. Everybody can be

thinking like a product manager. And so

very excited to talk about GrokBot and

the ways that we've been using it on the

SpaceX AI product team.

And so with that, wanted to share a

little backdrop of why we built GrokBot.

>> Yeah, so I thought it'd be helpful to

just kick off with some general context

and kind of why yeah, why GrokBot

exists. And

you know, we're thinking about this when

when putting this together and a few

themes sort of came up and a lot of this

was inspired by our internal usage as

well as what we heard from a bunch of

customers over the history of building

our products ourselves. And I think a

few themes emerged. One of those first

ones is that it sort of increasingly

felt that you wanted to work with

agents, and those agents weren't quite

at home in a chat box. I think the chat

box is actually really great. I use chat

boxes all the time. Really enjoy them

for all kinds of interactivity with LLMs

and AI workflows. But for something

about agents getting work done, it felt

a little bit limiting and constraining.

And so I'm really wanting to think about

what the right interface is and the

right way to interact with an agent

would be.

And I also think if you kind of

contextualize that to product management

work, it this becomes even more salient,

right? Like PMs are often trying to

focus on outcomes. You're often working

across multiple parts of the stack. So

you're coordinating with various

different functions at a company. You're

like kind of a general manager. You're

kind of thinking about design. You're

thinking about analytics a lot. You're

working with engineering. And you'll see

a little bit about that and how we've

set it up in the demo later on. But

really that PM work in particular seems

to go beyond this Q&A format that is

really native for the chat box. Um

the other thing that was a really sort

of emerging theme is that you know,

agents really need their own

environments to get work done.

We actually saw a lot of this with cloud

agents early on where giving agents the

ability to verify their work and verify

their output results in dramatically

better outcomes.

And so the idea was well like what's the

natural extension of that? And I think

it's giving agents their own

environments to do the work in. And I

think the more we started experimenting

and playing with that, the more we saw

that that could be a really powerful

powerful thing, too.

Um the last thing I wanted to mention

here is that, you know, it gets really

hard to babysit multiple agent threads.

We had a lot of folks internally who had

built their own sort of, you know,

coordinator agent that was then calling

out to other agents, but that wasn't

ever really done in a really first-class

way across products and

well we think Rockbot does a pretty good

job of this but really this idea of you

know as you're thinking about

multitasking you're switching between

meeting one over here on this topic to

meeting two over here on that topic

having some way to just keep track of

all the the mess that that goes on in a

normal work day

and I think we increasingly just started

to think of ourselves as well what if we

could you know work with our agents more

like colleagues versus working with them

more like software and that was kind of

a design brief for a lot of what Rockbot

is.

Um so to to carry on that theme for a

little bit four things we want to call

out here are on agents as colleagues

and why this distinction is so important

I think is because it's a lot of

inspiration behind the design of Rockbot

and how it works and how we're thinking

about the road map too. So

you know we think that colleagues really

focus more on tying tools together to

produce outcomes. Obviously sort of

connectors and MCPs and integrations

have been a really strong and an

important part of the agent story across

the industry but I think what the sort

of the exciting thing here is is making

it so that those you can tie those tools

together to get to work being done and

so I think that's just a really

important part of the story.

I think long-running context and memory

is really important about here routines

are really important here and so you

know thinking of first-class ways to do

those in the product really is really

great. Colleagues remember what you do

like you work with them they remember

what you do and so

having that be a part of the solution is

really important.

>> [snorts]

>> I think you know colleagues are

independent and so making sure agents

can be independent calling back this

idea of agents having their own

computers their own you know devices to

do things on their own software to go

work with. And then finally I just think

getting the messaging to feel right is a

really important part of this you know

when I DM Kevin on Slack I'm like

rapid-fire messaging and sometimes

throwing in some memes in there and

emojis and I think there's something

about that experience just being really

fun that makes it you know feel like

you're working with a colleague. So Um,

these things make sense for those of you

who had all the ones in the chat at the

beginning from from having used Grokbot,

uh, hopefully you've seen some of this.

Um,

but I guess that brings us to, well,

what is Grokbot? And I want to simplify

this after all of those words into two

things. Uh, so if there's sort of two

things that you're using when you're

talking about Grokbot or when you're

talking about it with your your friends

and colleagues, the first is that it's

an agent with a computer. Uh, and I

think we're really excited about the

computer part of this. It sort of gives

the agent its own machine, its own

environment to operate in, just like I

was talking about. Uh, and it really

feels like texting a colleague who has

its own computer.

Uh, and the second is that your team of

bots really are AI teammates. They're AI

teammates that you can give real work

to.

Uh, they finish jobs end to end. They

come back when they're stuck. And they

function just like colleagues. And, um,

I've been pretty excited to see a lot of

folks on X and other places talking

about how Grokbot seems to be more of a

doing agent. And so really excited for

that to continue to be what we build.

>> Yeah, I think one of the things that we

wanted to incorporate into Grokbot is

thinking about working with sort of your

best colleague or uh, your best coworker

or teammate. Uh, what is that like?

Well, they're generally very proactive.

They have a bias to action. They're able

to take real action based off of your

guidance. They learn on the job. And so

tried to incorporate a lot of those

principles directly into the product.

>> Yeah, and I think there's some fun ways

too. Like you can train them to do

certain things and kind of teach as if

you would teach, you know, someone

you're working with.

>> Absolutely. Very cool.

>> Um, do you want to walk us through what

the team looks like?

>> Yeah, absolutely. So, for the course of

uh, this demo, we're going to run

through a few of the really exciting PM

specific use cases, but uh, this really

generalizes for any type of product

builder. And so as we go through, be

thinking about your own workflows, uh,

things that you could potentially

automate, uh, different bots that you

could potentially build. And we can talk

about those in the chat and Q&A at the

end. Uh, one of the things that I found

GrokBot to be very helpful with is

keeping me focused. So, I have this

concept of the attention list, which is

not necessarily what are my like strict

priorities going into the week or the

day, but more like where is my

attention? And so, I like to think about

GrokBot as sort of sitting on top of my

shoulder, being able to review all of

the emails coming into my inbox, see

what I'm responding to, see the

different Slack threads that I'm

actually engaging with, maybe the Slack

threads that I need to know about but

are missing, and curate this attention

list that can then be used in different

ways. And we can talk about how that can

be a filter to keep you focused, how

that can be diffed against your priority

list

so that you can understand where your

your priorities may diverge from where

your time is actually going. And there's

a lot of really interesting ways in

which you can start to use this

attention list to really guide your

work. The other is, you know, for the

the blocking and tackling of new product

development, being able to very quickly

research a bunch of a bunch of of

different contexts. As Roshan mentioned,

like being a product manager, there can

be contexts which lives in Notion, in

Slack, in Gmail, and many other

surfaces, your support tools, your CRM,

right? And so, being able to have an

agent, a colleague, that you can work

with, who can help you collect that, and

formulate that into new product

directions, very exciting, and we'll go

deep into that use case. And then, of

course, we also need to be able to ship.

We need to be able to deliver new

products to market. And so, we'll talk

about some of the workflows and agents

that we have built to help us accelerate

shipping.

So, we're going to go through the team

that we have built. This is a

combination of things that Roshan and I

and the product team over at SpaceX AI

have found to be very useful for product

management.

So, let's meet the team. First, we have

Cora. Cora is our chief of staff. Uh you

can think of Cora doing all of the chief

of staff things, uh figuring out what is

most important both for us and uh the

team, being able to uh reduce the

distractions and block some of the

things, maybe auto respond uh to some of

the questions that are coming in, uh and

helping keep us focused and organized

both personally and as a team.

The next is Emily. Emily is an

engineering manager. Uh Emily has been

coached and uh

taught not to actually write code like a

good engineering manager, uh but instead

to manage a team of engineers. Uh and

so, these engineers are able to both

learn from existing uh code and PRs that

we have historically pushed across

SpaceX AI, uh and learn some of the best

practices uh and code some of the skills

from some of the best engineers across

SpaceX AI uh to be able to work just

like an engineer.

We also have Ashley. Ashley's probably

my go-to throughout the day being able

to ask questions of our data. So, Ashley

has access to our data lake and our data

warehouse and a bunch of different uh

user insight and user research

repositories. And so, you can ask very

broad and general questions and Ashley's

going to be able to build you that right

chart or that right slide or that right

insight uh to be able [clears throat] to

answer your question. And as a PM

throughout the day, uh many questions

will come up and being able to have

somebody that you can just DM uh and get

an answer back really quickly has been

exceedingly helpful.

Uh

Pete PM Pete is our little uh product

assistant and so, will help with a lot

of our RFC and uh product requirement

doc writing. Uh can also run that

research, coordinate across these

different agents, and really help us

build a high-functioning product org.

We have our designer, Pixel. Do you want

to talk about Pixel?

>> Yeah, I've been putting a lot of time

working with Pixel, actually.

Yeah, I think design is one of the

things that's really sort of hard to get

right with LLMs and with AI tools, but I

actually created Pixel inspired by a

recent

newsletter post from Lenny. And so,

there's a really great designer who who

posted a design system there and kind of

you know, took that and and put some of

the context into Crock-Bot, and you can

kind of

have supplemented that as well with some

other design best practices. And I think

it's been like the one of the more one

of the more fun ways to do design. And

Pixel has access to Figma, of course, so

it can get in get into Figma and help

make designs and slides, and also has

access to our design system. So, that

when we want to make something that you

know, matches the Crock-Bot brand or

matches other parts of the Space x AI

brand, there's a really strong starting

point and then a really strong set of

skills that Pixel's built over time to

understand how to be creative and

flexible and able to yes, be be a great

AI designer.

>> Very good. And yes, as Ursion mentioned,

the PMs often wear many hats, and so

being able to have a colleague and agent

that you can turn to for a lot of this

work is very helpful.

And also for people in product

leadership, people who may be building a

product organization, also very helpful

to have a recruiter on the team. And so,

that is Ray. Ray can help us find

amazing talent and help steward

candidates through our product interview

process.

One of the questions you may be asking

yourself is why are there many agents?

Could this just be one agent? And I

think we've sort of distilled this down

into a few sort of helpful principles of

why multiple agents or multiple

colleagues really makes the most sense.

I think one is just if you sort of map

it to today's organizations and the ways

in which a company is built, you

generally have specialists who are

really good in a particular area. It's

also very helpful for referenceability,

right? So knowing who to turn to when

you have a data question versus a design

question and knowing that those are

specialists in those areas. You also

have scope memory, right? So each agent

is learning on the job. You're coaching

them, you're giving them feedback and so

they're able to learn different things

and then you can also run many of these

agents in parallel and we can talk

about, you know, how we DM with our

agents and sort of this new workflow. It

feels a lot more like, you know, just

chatting with a colleague as you may in

Slack or Teams.

>> Yeah, the referenceability one is huge

for me cuz it feels like I I just need

some way for my brain to stay on top of

like what I what everyone's working on

and so I know we talked a lot about that

colleague analogy, but I think that one

it it really helps for me to have tried

having just one agent and like my brain

can't handle it. You know what I mean?

So

I think that's huge. So

Cool. All right. Well, we want to get to

maybe some exciting demos and so we

thought we would show you this team in

action to build an example feature. So

we'll pan over to GrokBot here and just

to orient you if you were if you were

one of the folks who put a zero in the

chat when we were gathering meaning you

have not used GrokBot before. Well, this

is GrokBot. We really hope you try it

out. We might have some fun credits

coming your way I hear towards the end

of this so stick around if you want to

make it really easy to try it out. Um

but essentially GrokBot is meant to

really just be simple. It's meant to be

simple where you have a bunch of bots on

the left here. You can pin those bots

for the ones that you're talking with

most and will you hopefully you're

seeing some names here that we just

talked through so you see our chief of

staff Cora, you see Ashley our analyst

PMP and Emily. You see our team of

engineers down here. You can also group

and organize these bots, give them

labels, customize them, their avatar.

So, really just a chance for you to

personalize and make this yours. Um and

the other thing I wanted to call out

while we're here is a lot of the power

that these bots get come from the tools

that they can connect to. And so, we

talked about particularly for product

folks how those tools are just such an

important part of our job, right? Like

I'm living across, you know, 10 SaaS

apps each day. Um

and so, I wanted to show that, you know,

this marketplace is a really important

part of this, too. And so, um obviously,

you can have uh sort of plugins. We have

categories where we've broken these

down, everything from agent

orchestration tools to inbox tools, uh

you know, MCPs that are in here, there's

a bunch of research-specific tools, um

documents, if you're using Coda or

OneDrive, design tools, etc. And so, um

really one of the things that I

encourage folks when they're getting set

up for the first time is to just go

through this and, you know, link all the

tools that you're using uh and kind of

get them all connected. And once you do

that set up once, sort of every agent

benefits, and I think that's really

that's really helpful. So,

all right. So, um what do we want to

show? I guess maybe uh should we start

with a building a feature and and see

how that goes?

>> Yeah, let's do it. So, let's start with

uh

just chatting with our chief of staff

and get a feel for uh how you can chat

with Cora here. So, uh let's just see

what's in my inbox.

>> Yeah, classic PM classic product person

work. Let's help me triage my day.

>> And uh for the purposes of this live

stream, this will be a demo environment

here. And so, uh we are asking the uh

our colleagues here in GrokBot to

hallucinate a little bit and uh create

some fictitious data, but these are

going to be simulating our real

workflows and the ways in which we

>> is your actual team that you're using

every day, right?

>> That's right. Yep.

Uh okay, so we can see our inbox pulse.

Uh we have some uh questions coming in

from Parker on voice, uh a few questions

on the marketplace,

uh and then let's see uh what have I

been focused on this morning?

And this is where we can start to curate

our attention list here. And so this is

going to go through Slack and Gmail and

all of the uh different updates that

I've made in Notion this morning uh and

figure out sort of where is my

attention? What are the top things that

I have been spending time on? So

>> Yeah.

>> Looks like I've been spending time on

local-to-cloud handoff, uh marketplace

trust, and a partner launch kit, and a

bunch of other things that I've already

done this morning, which is incredible.

>> Productive morning and it's what, 9:00

10:00 a.m.?

>> Yeah, yeah, yeah.

>> Uh if you haven't read it, Kevin has a

really great post on X detailing how

this attention list works. And I think

it's really fantastic. It's sort of

rather than a to-do list, it's the sort

of uh have the bot shadow you. Uh and I

think you get a lot of really

interesting insights from that and then

can kind of steer these agents towards

helping you get you unblocked on the

things you're working on most.

>> Mhm.

>> That's the attention list in action

right there. Yeah.

>> Very cool. And so we what we've seen is

uh Cora has been reactive to the things

that we have asked, but it is very easy

to turn Cora proactive. And so we can

say, "Every hour triage my inbox and

clear out anything I don't need to pay

attention to."

And so what's going to happen

uh under the hood here is Cora is

actually going to set up what we call a

routine.

>> You want to talk about routines?

>> Yeah, so routines are ways that you can

automate agent tasks. I think you can

sort of think of them as just scheduled

tasks or repeated tasks, but they're

natively uh agentic with our with Grok

bots. And so um yeah, you can ask agents

to create these. Uh sometimes if an

agent sees you do this these tasks or do

some tasks over and over again, it'll

suggest or automatically create a

routine. You can view and manage the

routines from the UI and be able to

click into them. And so maybe we can

show that, too.

Um

yeah, there you go. Um if you click I

think it's the right arrow there.

Oh, no, that hides them. Okay, well,

got routines. Uh, and so yeah, you can

you can look look at routines. They're

automated tasks.

Uh, and what's great about those

routines they are also maintain

connectivity to all the tools that you

connect. So, um, we'll show an example.

Oh, there we go. Yeah, we'll show an

example of um, this a little bit later

on, but uh, for our analytics use cases

one of the things that I love is asking

a uh, my analytics bot to keep sort of

hourly data or hourly tracking for new

features that we launch and that way you

can sort of get a get a metrics

dashboard whenever whenever you need on

demand uh, instead of having to go to a

dashboard somewhere on the web. So,

there you go.

>> Very cool. Uh, and so uh, while Kora

sets that up, we are going to I think

build a feature together live

with our Grok Bot

colleagues. Uh, and so to kick things

off, I had asked uh, what are some of

the top features that people have been

asking for uh, on Reddit and X. And so I

think we have a pretty good list uh, of

uh, research uh, PMP went out and did a

bunch of research across social, uh,

also looked at our uh, internal Slack

discussions, our feedback channels. Uh,

we also have a uh, amazing user research

database of all of the uh, interviews

that our user research team is out doing

with users and customers. And so there

is a lot of feedback coming into our

product team.

>> Yeah, and synthesizing that I think is

just its own job sometimes. One of the

things that I found most useful about

bot on that UXR phase is being able to

ge- generate insights from all the data,

but also just like keep an eye and a

pulse on what's new. And so normally I'd

have to go in and re-prompt or re-prompt

or re-prompt when a new user research

interview comes in or every morning with

latest Slack updates, but with those

routines we just showed or with a quick

prompt like this you can kind of get it

all at once. So, I think that's awesome.

Any cool ideas in here you see?

>> Uh I'm loving two-way voice, but uh

while we have you all here, maybe you

can throw in your top Grokbot feature

requests for anybody uh who has been

using the product.

>> Drop them in the chat. We will add them

to our user research database.

Connect Grokbot to Tesla, account

switcher, ability to switch models.

Sync of draft messages, okay.

>> Mm, okay.

Voice.

Pin to top in note thread, okay. Two-way

voice, I'm seeing a lot of votes for for

two-way voice.

>> Let's try it.

>> Okay, so why don't we uh do a little bit

of research up front? Let's not just

dive in to building, although that would

be very exciting. Uh so, why don't we

ask

uh

look at the speech-to-speech

benchmarks

and find out who are the top model

providers

for speech-to-speech.

So, we want to be able to figure out

sort of which model, how we want to

actually implement that. Let's go figure

out uh who is top here, and while we do

that, maybe we could work with Ashley

and ask a few data questions.

>> Yeah, sounds good. So, um I guess

uh let's switch over here. So, yeah, I

think the things that we

I'm thinking of the things that we might

want to be that we would want as input

when when thinking about a new feature.

And maybe some things we could start

with is understanding the market size of

the product and kind of the opportunity.

So, we could start by asking uh help me

understand the market size uh and

opportunity

uh

for voice products. I'm interested in

the TAM.

Uh

yeah, let's start with that. Uh

And one of the things that I really love

about having a purpose-built sort of

analyst or data-focused bot is that over

time it just really smart about how you

like your data. We actually have some

custom skills here that help us format

data in a really nice way and make it

kind of match our match our brand. So

when we're going into, you know, putting

together putting together slides for a

meeting or kind of be able to share data

with your colleagues that kind of looks

like it feels like that what what

everyone's used to.

I also think every organization and

every team, you know, does data

differently, right? You have different

KPIs, different metrics, different

maybe you're you're annualizing your

revenue in a different way. And so

having a bot that can sort of be that

go-to for all that I think is is really

great and allows you to kind of

establish some of those patterns. So it

looks like Ashley is grinding away here.

>> Yeah, yeah, yeah. Running some queries

for us. The other thing that I love

about Crock-Pot is it can multitask.

These agents can multitask. And so you

can ask multiple questions in parallel.

And so while Ashley goes off and does

this research for us,

we can also ask additional things like

we have a bunch of voice capabilities

already across our products. So show us

what percent

users use any voice mode across our

products.

And while we were typing that, Ashley

came back with some insights.

>> Yeah, it looks like we have maybe a $48

billion market ahead of us. That sounds

pretty nice.

And yeah,

12.5 billion SAM. And it looks like

Ashley here because over time we've

we've taught Ashley how to make charts

that we like has has put together some

charts for us. So some some analysis

here on the market size and it looks

like we're seeing what voice API and

infra is pretty close to dev.ai and

coding agents. So nice opportunity for

us. And obviously

yeah, I think there's some really good

stuff there.

Yeah, it looks like we have some some

caker data, too. So, all right. It seems

seems like a worthwhile opportunity, I

think.

>> Very cool. And uh, one very useful pro

tip is to always ask the agent to uh,

bring its citations. And so, that way

you can inspect and uh, maybe there's

some additional editing or follow-up

questions that you want to provide on

the underlying data set that agent had

used. Uh, and so, oh, look at that.

Okay, so we got

uh,

voice TAM. Okay, so this is a

visualization for that voice

TAM here. And then we get the uh,

percentage of voice weekly active users

across our different products.

>> the kind of data that I think can be

really hard to obtain sometimes by

yourself when you're just a solo PM or a

solo product person. You're often

reaching out to a data team. You're, you

know, making sure you can query the

right databases in the right ways. And

so, um, I use this kind of stuff all the

time. And it's a really great way to

say, "Okay, cool. So, looks like we

maybe, you know, see good penetration of

voice products across our

voice use voice features across our

product surface. Uh, maybe makes sense

for us to double down there with a

feature."

>> Yeah, very good. Uh, we'll also call out

that uh,

GrokBot right now internally in SpaceX

AI is sitting on a great foundation. And

so, one of the things which uh, comes

through in this example is you really

need a uh, good clean data set and a

good data lake to to connect GrokBot to.

And so, there's been a lot of hard work

on our data engineering team uh, who are

using GrokBot uh, to build a lot of

these data sets and uh, curate a lot of

this good data on top of which Ashley

can query and provide trusted insights.

So, there's a lot happening underneath

the surface uh, to make sure that you

have the right foundations for agents to

be most successful for your product

team.

>> Totally. Like having canonical data

tables, making sure that there are some

skills made available to the agents to

know how to avoid common querying

mistakes. Uh, and all that stuff that

you can sort of obviously encode into

these agents, but it looks like P and

Pete's come back with some data.

>> Yeah, let's pop back. We were asking

who's top of the leaderboard for

speech-to-speech. It looks like Grok

Voice Think Fast 2.0 is top of the

Frontier Native Audio. So, maybe we

should use that model. Yeah.

Okay.

>> And I think Frontier models are great.

So, yeah, we should use them.

>> P and Pete is also equipped with a skill

and a template for our product

requirement docs. And so, we have a

weekly product review that we can bring

any open topic or any new idea that we

want to discuss as a product team. So,

should we work with P and Pete to to

write one of those?

>> Yeah, let's do it. We should just ask

ask P and Pete to help us.

>> Let's

write a PRD

RFC for voice mode in Grok Bot.

>> And I think he's already connected to

Notion, right?

>> will get put into our Notion. That's

right.

>> Okay. Awesome. Awesome. Yeah, so P and

Pete here has contacts on how to write

really great PRDs. And so, P and Pete

knows that a great PRD is not just a

bunch of requirements on a doc. It's

insight from user research, which we

just looked at together. It's maybe

insight from the industry and landscape

that maybe Ashley just helped us put

together.

And obviously thinking about prototypes

and increasingly we find ourselves

prototyping product a lot early in the

process

for how a you know, how something could

look and feel in the product. So, we'll

see what Pete comes up with.

Is there something else we should show

in the in the meantime here?

>> Why don't we have Pixel start cooking on

some design Would that be helpful?

>> That's a great idea. Yeah, actually I

might just ask Pete to do that. So, one

of the cool things about Grok Bot, can

you also loop in Pixel to help with

design?

So, yeah, you can just message message

these folks. They'll they'll keep

working in the background and can handle

a multitasking, but you can also tag

other bots and I find this to be really

fun. And so Pete here, oh there we go,

is going to loop in Pixel and Voice Mode

Design

and is still drafting a PRD. So Pixel,

as we talked about earlier, is our

designer who's been trained on design

systems and how great design works, how

to think about iterative design and

design thinking. Obviously, a lot of

that knowledge has come from an amazing

set of human designers to help help make

it really great. But in our case, we can

really get a head start on some of this

process and you'll see here's what

Pete's doing.

And you can we can you can kind of spy

on their conversation a bit.

So it looks like there's some context

that's shared,

some seed strings, a visual brief, some

product review, and a mock section that

looks like it's empty that will

hopefully get filled out. So

all right, we can close our read-only

chat here. Looks like Pixel's briefed.

>> Amazing. Yeah, I think anybody who's

spent a lot of time thinking about how

best to prompt agents, one of the things

you learn is agents are actually very

good at prompting other agents and

working with other agents. And so we

have

just put these two into a group chat and

PM Pete is able to actually share all of

this context and some of the things that

PM Pete has learned directly with Pixel

without us having to copy and paste or

move context around. Agents can just

share that behind the scenes.

>> I think one of the cool things here,

too, is as they're sharing, but also

each agent still has its own memories,

its own context, its own routines. And

so, you know, if we really work with

Pixel over time to teach it specific

behaviors, let's say, you know, always

use dark mode, um Pixel's going to

remember that even if Pete is the one

messaging Pixel in this case. And so, um

I think that also just takes a lot of

the mental load off. You can kind of,

you know, enforce your preferences once

and then have those preferences be

reflected in all the future work you do.

And it's sort of like really makes that

initial setup worth it, I think.

>> Mhm.

>> Mhm.

>> Cool. And are there specific routines uh

that you like to set up across your

analyst?

>> Yeah. So, one of the ones that I really

like, and while we're cooking on that

that doc, is I really love having

regular product monitoring for new

features. And so, you know, as we're

launching new features uh into Grok Bot,

being able to see how they're going is

really helpful to the product team so we

can, you know, both just keep an eye on

usage, but also just know where we

should double down our investment. And

so, um one thing that I'll do very often

that we can ask you to help out with

here is um we recently launched support

for the

XMCP

in Grok Bot. We did that on Saturday,

actually. Uh can you send me an hourly

pulse of how installs look? And to

start, give me the last few days of

usage. Um

And so, this is me setting up a routine

here. I'll do this very often for new

product launches. And so, every morning

I'll wake up and have maybe well, for

the hourly ones, I'll see them every

hour, but also do like a morning brief

and kind of see how are all my products

doing? Where like where where am I

seeing more usage? If I see a metrics

dip, what's really great about this is I

can also just instantly sort of in the

chat be able to inspect and analyze what

what what what happened. Um what's

really great about analytics use cases

like this is because it's tied to the

data lake, as Kevin mentioned, the data

warehouse under the hood. Um we can also

look at product metrics from other parts

of our product suite to try and explain

what's going on. And so, um yeah, it

looks like we have a routine that's

created. That's our hourly pulse.

Looks like some installs happening.

And there's our Yeah, there's our every

hour.

>> Very cool. Very cool.

>> Yeah. So, yeah, and this is this can be

just a really great way, I think, to get

um analytics to be more of a passive

part of your day-to-day. Uh I before

something like this, I'd find myself

just like opening a dashboard all the

time, refreshing it, keeping it pinned,

having it on a wall somewhere. Uh and

now I just use my bots. So.

>> Very cool. And uh while we were setting

up that routine, PM Pete has returned

with our product review doc.

>> Cool.

>> Uh and so we have a draft of uh our

Grokbot voice mode. Let's take a look at

this. Uh so here is the opportunity.

Uh you could even pull in those uh TAM

insights from Ashley. Here are some of

the customer pain points pulled from

social media and the requests.

>> Didn't have access to the chat.

>> Yep, that's right.

>> Here, but that's okay. Uh and here is

the proposed solution here. So live Grok

mode voice mode on Grokbot, which is

very cool.

>> I like this. Yeah.

Um okay, looks like there is still a

placeholder for Pixel to pop in some

mocks, but it it knows that Pixel needs

to do it, which is really nice. So we

could always ask our PM Pete to make

sure that that gets completed. Um and I

really like this, too. So it's sort of

like here's the doc and here's the TLDR.

So I can refine the TLDR a little bit.

Oh wow, there we go. There are our

mocks. Just I was just saying I was just

saying that, yeah.

>> Pixel has just handed the mocks back to

PM Pete.

>> Let's see how he did.

>> Amazing. Look at the storyboard.

>> I like this. The little little bot in

the bottom.

>> Okay. Whoa, went with the huddle

directive. Very interesting.

>> Yeah, I mean I do that a lot in Slack,

right? We we we huddle each other, so.

>> Listening, little back and forth.

Okay, like a little menu here.

>> Menu crisp.

>> Oh, even went to mobile. Even went to

mobile.

>> Started from action button. Okay, yeah.

I mean this is like a really good

starting point, right?

>> Yeah, yeah, yeah.

>> Probably could use a little bit of

polish and consistency, but definitely

enough to make a great prototype. Should

we try to build it?

>> Sure. Let's do it.

>> Uh so Emily's our engineering manager,

right?

>> That's right.

>> All right. And then what do we think?

Should we ask Pete to ask Emily to build

it or just go straight to Emily?

>> Let's just have them work together.

>> Okay. So one thing that you can do in

Grokbot is you can actually just create

a group chat. So we'll add PMP and where

is our There's Emily.

Um I will just do our PM and EM standup,

right? Uh and say, "Hey guys. Hey folks.

Um Pete just put together

a PRD for voice mode. Can you all sync

up?" And Emily

start building this.

And so yeah, this is now you can see

that we're in a we're in a group chat

with Pete, Emily, and ourselves here.

And uh what's really cool about these

group chats is that they feel just like

talking to a bunch of colleagues at

once. And so we're getting the contacts

from both of these agents. Obviously,

earlier we saw agents can sort of tag

each other and then prompt each other.

Um but it will all often use something

like this as a way to just be able to

see the contacts of both agents in one

place and be a little more hands-on,

right? So sometimes we'll uh you know,

ask my chief of staff to uh you know,

talk with the other agents and get

something done. For I think build cases

like this, it's really nice to be

hands-on and get the get the agents to

be talking to each other. So.

Cool. Very cool.

And we will let them work together down

here. And uh what Emily is going to do

as a next step uh is actually distribute

this work. So uh we'll take the uh

product requirements doc or RFC,

translate that into uh discrete

technical tasks, which then she will

hand off to her team of engineers, who

will each get a ticket.

Let's see. Oh yeah, it looks like

they're chatting. Checking on them.

There's Pete confirming the source of

truth. And then Pete, who's put together

our requirements, has the V1.

Any good product starts with a V1 and

MVP prototype. And so it looks like

Keith got that figured out. And

they're thinking about it. Yeah, it's

your stuff that's out of scope, which I

love. Always always great to see that.

Cool.

And they should be cooking.

Very good.

And one of the amazing things uh of Grok

Bot is it is able to actually spin off

cloud agents to do coding tasks. And so

uh you can think of Grok Bot as being

able to hook into a specific coding

harness

uh and a set of agent cloud agent

environments, which is really optimized

for coding tasks. And so you can have

this great handoff and these managers of

cloud agents going forward.

>> Yeah, I think this goes back to what we

were talking about earlier with what we

saw internally, which is a lot of uh our

really great engineers found themselves

managing multiple sort of even dozens or

hundreds of agent threads over the

course of a day. And um sort of were

creating their own versions of

orchestrator or coordinator or sort of

manager agents. Uh and so one of the

ideas that inspired this particular

feature in Grok Bot was making that feel

really native. Uh and you know, there's

a bunch of debate and discourse around

how much people are looking at code

these days versus, you know, if you're

looking at outputs and outcomes. And I

think this actually feels like it could

be a really, you know, interesting way

to write code in the future and and even

now, but uh basically, you know,

focusing on outcomes, asking agents to

coordinate with each other. Um and then

sort of giving agents the ability to go

and spin off these these, you know,

coding focused environments where uh

they can get a lot of things done. So uh

yeah, looks like they're cracking away.

>> Very good. So it looks like uh Emily has

actually given me a task to unblock one

of the engineers. Uh looks like there is

an approval that is required on my side.

And I think this is a good example where

uh these types of handoffs uh agents are

really uh trying to push as to be as

active as possible on your behalf. Uh

but they can sometimes run into access

issues or need your help to log into a

tool that you use. And we want to equip

our agents with the exact same tool set

that we have access to so they can be

most productive. And so we'll often get

questions from the

team here asking me to log in or whether

they can have access to a particular

tool that would make their work even

more productive.

>> Yeah, I think it's really helpful as we

talked about earlier to make sure

everything's connected. Obviously, over

the course of writing software,

depending on a feature you're writing,

might need to be able to validate

validate those in different ways. And so

looks like our engineers are cracking

away here.

One other thing I'll also mention that

we didn't talk about much earlier is you

can do all of this on mobile, right? So

we saw that mock-up of GrokBot on mobile

voice, but I'll often do this on the go

where if I have an idea for a feature, I

used to you know pull up a notes app and

then wait till I got back at my desk and

then kind of type that into a prompt and

then write the doc. Um, but I can kick

off this entire engine when I'm just on

the go.

You know, just I can talk into GrokBot

with the the speech-to-text flow and

maybe soon a voice flow.

But really being able to access this

anywhere and on any device, I think it's

been a really huge unlock. Like

sometimes inspiration strikes at the end

of a meeting and you just want to be

able to kick off an idea or kick off a

prototype. And once you have the system

set up, you can really go from zero to

prototype really really quickly. So.

>> Very cool. And so it looks like Eileen

and Emily have

>> Who's Eileen? Eileen's one of our

engineers.

>> One of our engineers and Larry are all

coordinating on this work. And so

looks like they are

looking into the best way of

implementing pool hint.

And so this is just a great example here

of really the autonomy and ways in which

GrokBots can

coordinate their work, work together on

some some larger features.

>> Yeah, and if you want to set up

something like this, I think you know,

you're seeing that a bunch of different

people or agents are being pulled into

the chat. And that's because of the way

that Emily is set up and these engineers

are set up is that they know they can

tag each other. And so, over time as

Emily has grown in in her role as as the

EM here, she's learned that she can task

out certain things to sub agents or

other agents that she can loop in folks

to help delegate work and then be able

to review that work. So, sort of

creating like a task and reviewer

paradigm. And then also be able to sort

of

sort of test features independently as

well. And so, we didn't this case didn't

have to tell Emily to go and tag these

other bots, but that sort of happened

organically as a result of how we built

this. And so, you know, for your

development workflows, if you're excited

to use Grokbot for this, obviously, that

might look different. That might look

like one that might look like two EMs,

that might look like three EMs, that

might look like just a fleet of

individual engineers where you're the EM

role. But really the point here is that

you can set these bots up to do whatever

makes the most sense for your workflows.

And then kind of have that pattern be

enforced on on future runs. So, how far

do you think we'll get?

>> I don't know. We'll have to wait and see

where this team ends, but it is very

cool to see them in action.

I also think one of the things that we

have thought a lot about is sort of at

what point should you as the coordinator

and PM on the team actually take back

control. And I really like to think

about

really offloading a lot of the

toil and maybe

lower sophistication, lower quality work

to my agents and sort of building up

context as we go.

Still being sort of that last mile of

reviewing the you know, research that

agents have put together,

refining that product doc that we saw to

really make sure that it represents like

the best strategy and the best product

that we could potentially build. And

then obviously there is a lot of code

review that goes into ensuring that the

code that our our agents are writing can

actually make it into production and

really thinking about that full pipeline

from idea through to code in production.

And what are the sets of systems and

agents and

bots that you want to bring to bear to

really accelerate, yeah.

>> Yeah, I think that review and refine is

a really good way to put that. I think I

find myself doing more of that work and

I think there's always some stuff I

catch and so I feel like it is a really

important part step in the process here.

But I think what's really changed with

with something like this is that I can

I can kick off a lot more work than I

used to.

>> Mhm.

>> It feels like I can manage

[clears throat] a lot more work than I

used to cuz I can delegate a lot more

than I used to. And so that that sort of

freeing up more time for me to focus on

review actually feels like where I want

to spend the most time as a product

person. Like I want to be thinking about

what's next and trying out new ideas and

I don't want to be thinking about, you

know, the GitOps under the hood for me

to be able to make sure I'm on latest

for the design system changes that went

in last week. Like I want that to be

abstracted away and so

I think one of the really powerful

things here is you can do that. You can

focus on just being this creative

product person who's thinking about what

you want to build next and doing that as

quickly as you can. So.

>> Very cool. Well, I want to make sure

that we have time for questions and

discussion at the end, but I do see one

other agent here which looks like Xbot.

What is What do you think Xbot is? Xbot

is actually a agent which manages my

XDM's. And so what we are going to do is

for the next 10 minutes anyone who DMs

me N2 Parco

on X, let's send them a link which will

have a $100 promo in credits. So let's

fire that off and let's let Xbot respond

to anybody in my DMs with a little promo

code for Grokbot.

>> Awesome. And yeah, these bots can do so

much. They have so much uh to connect

services. And so yeah, that's it's great

example.

>> Very cool. Cool. Uh and for anybody

who's not streaming on X, uh you can

also scan this QR code for uh joining us

today. Uh would love to hear what you

build uh and very excited to get Grok

Bot in your hands.

>> Yeah, thanks so much. Uh we'll see if we

can get a version of what we just built

out maybe after the after the call, but

I think that the takeaway here is that

these these systems are really powerful

and they're systems that are really uh

the sky's limit and we've seen all kinds

of different teams put together. Um if

you're interested in seeing this team in

a bit more of a text format, definitely

check out Kevin's post on X uh to take a

look at how you could build this. And

actually, when Kevin first wrote this,

the way that I set up my team to look

just like his is I just pointed one of

my Grok Bots at that post. And so you

can you can do that too if you want this

team to power your workflows, um point

them at that post, they'll set this all

up for you, ask you some questions about

how you do your work, and you're off to

the races. But um yeah, very excited to

see what you build, very excited for

everything that's to come in Grok Bot,

and thanks for joining us.

>> Amazing. All right, Noah,

I believe you have been curating some of

the top questions coming in as we went.

Would love to hear what's top of mind

for the group.

>> There's a lot in here around just what

is the future of Grok Bot look like

um as far as

the platform itself, but also how does

that connect with things like Cursor

Cloud agents and stuff like that. Uh

it'd be great if you guys could talk

through maybe

how do you see this kind of evolving

over time?

>> Yeah, I I can start out with that one.

Um really I think we want Grok Bot to be

the platform for you to do your do you

know, do your most useful work. And so

the the brief really is those two things

that we showed at the beginning, which

is focusing on giving you a really

useful agentic colleague, um but the

meta goal here is making it so that you

can do more, be more productive, and you

know, get more done. You you you know,

feel feel better about the work you do.

And so, you know, I I think everything

we're building is in service of that

mission, which is really helping you get

things done. And so, obviously for

coding workflows, that means

things like cloud agents are and making

that experience really great. Obviously

like a lot of stuff just around how the

products interact together that we that

we're thinking about.

But then I think there's a a whole bunch

more, right? Like there's the coding

loop but then also the rest of the

system and I think excited to explore

more stuff there. But long story short,

you know, we talked earlier and showed

you earlier how these agents, these bots

can create cloud agents. We're really

excited about that capability and

continuing to support it. So, yeah, I

don't know if you had anything on the

cloud agent side of things.

>> Yeah, I think there there the sort of

two big dimensions. One is increasing

access for bots and so ways in which you

can work with bots in Grok bots in new

ways. So, one example of this is we just

introduced our Android app this morning.

And so, bringing this to

new surfaces, new platforms and being

able to work with your team just like

you do colleagues. I think is a really

important tenant. And then the other is

allowing our team here to be able to do

more. And so, a lot of that means new

tools, new connectors

and new ways of working that are going

to allow your Grok bot team to be able

to take on more and higher complexity

work over time.

>> Another common question comes around

some of the context management piece and

I know that that's like very different

than how context management has

typically been done within cursor and

many other platforms.

Be helpful if you guys might have

touched on this a little bit but to

frame some of the mental models people

can think about

when effectively like managing the

context of these agents since it is such

a different paradigm.

>> Yeah, we spend a lot of time thinking

about call it like the memory system and

routine system for these agents.

And particularly because they behave I

think differently than a lot of other

agent products do. They you know have

their own computer, they have a

persistent computer, they can kind of

execute code in the environment, execute

tasks in that environment, save files to

disk. And so I would say from a context

perspective the vision here is that you

should never have to worry about

context. Right? And I think that's maybe

the again like the way we're thinking

about this is sort of historically

you're always watching a little meter or

thinking about how much you're using for

a particular chat. And I think the goal

here is that these are very sort of

incredibly long-running agents. Like my

conversation with my chief of staff has

been running for months. And so really

you shouldn't the goal here is that you

shouldn't have to worry about managing

that context and that these agents

should get smarter over time and at

least from our usage internally that

longitudinal behavior proves out, right?

Like I work with my agent who remembers

the stuff that's most recent, some of

the older stuff that's like kind of

fallen off the road map list for

example, like my bot doesn't bug me

about that and that's kind of what I

want. And so really I would say that

that's at a at a broad level how we're

thinking about it. And obviously a lot

more a lot more to come there. So.

>> Yeah, and anybody who's invested heavily

in agent context systems, in your

skills, all of that ports over to

Grokbot. The other thing which is

relatively unique and something that may

require a bit of a adaptation if for

those who are used to working with

agents exclusively via the text box

is your Grokbot team can learn on the

job. And so being able to teach it,

correct it, show it things. Actually you

can record workflow on its computer and

it can learn from the ways in which you

work. And so there's sort of this whole

new dimension that opens up as your team

becomes more autonomous of ways in which

you can teach it and extend it and make

it better and better as you do your

work.

>> There's a lot of curiosity just into

like how our team is using Grokbot as a

whole.

People have asked like what portion of

like Cursor or SpaceX in general is

using Grokbot in their work. You know,

we talked a lot about the PM use cases

here, but there's also questions of

like, "Hey, how much of of the features

that get shipped or designed by Grokbot

versus you all?" Uh it'd be great to

maybe pull back the curtain on just like

the the general usage there.

>> Yeah, I think one of the things that we

saw really from this product's inception

was internal product market fit, which

is really a good signal. And so we have,

you know, a large go-to-market team,

very resonant among different parts of

our go-to-market team from finance

[clears throat] to our sales team,

to our post-sales team being able to

orchestrate a lot of what goes into

account management, to our sales

pipeline, and even to running our

business. And so yeah, from the get-go

it was

high levels of of product market fit.

The other thing which I think was sort

of novel here and not expected

is just how useful Grokbot has been for

product and engineering teams

and technical teams. And so really the

connection to cloud agents and having a

really great coding harness alongside a

autonomous colleague has been really

magical and we've seen

a significant number of our

merge pull requests these days are now

coming from

colleagues in Grokbot actually

initiating the work with cloud agents.

>> Yeah, I think that's super true. And

then, you know, Noah, to your point

around pulling back the curtain a little

bit, I think a lot of the ways that we

find ourselves interacting with Grok

bots is sort of through those

connections that we've been talking

about. So, a lot of times folks will

have a Grok bot helping them monitor

Slack. And so, you know, no matter what

work you're doing, there's like tools

that you sort of there's like that that

that stuff you need to do to just do

your work, right? And so, there's things

like connecting to Slack or you know,

using a particular like HR system or

whatever. And so, I think we we see a

lot of use cases where folks will

monitor the most important issues in

Slack. And so, they know when they need

to jump in there and their bot helps

with that triage. So, regardless of

role, there's there's that there's that

type of use case. And then, you know, I

think people are also having a lot of

fun with it, right? There's a lot of

ways that we're you know, adding you

know, like

communicating with each other in in

Slack and other channels. And and I

think that's also really great. But

yeah, in general I'd say there's there's

a lot of connecting to other system

stuff. Like a lot of our interactions on

linear and ticketing are happening with

bot like bots initiating those tickets.

A lot of times like when I'm like

looking at the queue of work that I need

to burn down in a given week,

I'm using using a bot to help me triage

that. And so, I think, you know, across

the across the suite but also for

specific tools that's been really cool.

>> Uh one or multiple emails coming. When

can people have multiple accounts?

>> We're thinking about it. Nothing to

share or promise yet, but definitely

hear the feature request.

And please do keep those coming. Like

our team is all over X and all over

like the feedback we're seeing in the

industry and it it really really is

helpful. We're like what you just saw

where we'll look at how how often a

feature is used and get some some usage

data on it.

It's something we're doing all the time.

Like we are truly doing that hourly and

daily internally.

And so, yeah, please do keep those

coming. And we're we hope we'll have a

lot of exciting stuff coming soon.

>> Yeah, yeah. And feel free to reach out

to us anytime as you all get going. We'd

love to hear your experiences, share the

bots that you're building, and would

love to hear any feature requests. So,

yeah. Thank you so much for joining us

today and excited to see what you build.