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TLDR
Claude Managed Agents (CMA) enables building a research agent that can delegate to sub-agents, use outcomes for self-supervision, and share memory across sessions, as demonstrated in a workshop analyzing SEC filings. The platform handles infrastructure and event storage, allowing developers to focus on product logic.
Key points
Claude Managed Agents supports multi-agent coordination where a main agent delegates tasks to specialized sub-agents.
Outcomes enable self-supervision by having a sub-agent evaluate the work against a rubric and retry if needed.
Memory stores can be shared across sessions for knowledge reuse.
Vaults provide secure credential injection by swapping placeholders for real values only on allowed domains.
The deployments API allows scheduling recurring sessions for ongoing analysis.
Techniques
- multi-agent coordination
- outcome-based self-supervision
- memory sharing across sessions
- dreaming
- vault-based credential injection
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Transcript (captions)
[music] Thank you for the presentation. Out of curiosity for the design of the session in um first iteration and second iteration what has changed?
>> Good question. Gabe, do you want to take that question on what changed within uh the storage of the session between the first and the second iteration? >> Um,
sure. The the the main thing that changed was that we wanted to abstract away as many things and comp compartmentalize them into different components um to make it so that we
could extend the storage mechanisms for future events that we wanted to store uh without tying them you know so tightly coupled to the mechanics of the session work. Um, so we basically apply the same
principle of building and virtualizing interfaces all the way down uh not only across service boundaries but also at the event layer. >> All right, thank you. Um, so it sounds
like maybe everyone might not be able to follow along, but hopefully um we can find a way to share out these slides and you can try it out at home or if you have your phone, maybe you can try to
pair it with your hotspot and see if you can follow along. Um, all right. So let let's try this one more time. And we're going to say, build me an agent that tells me jokes every time
given a topic. And then you just hit enter. And you can see Claude is coming up with a system prompt. It came up with a name, a description, chose a model. You can
like go change this model if you want to. and um give the agent access to the agent tool set. And then you'll hit uh create agent. That should basically call the v1 agents
endpoint to create the agent. Okay, that that got created. Next, we're going to configure the environment uh which is the template for the sandbox.
Uh we're just going to create a new one and then we'll just give it unrestricted access for this one. But you can obviously when you're building a production agent you you want to tighten
the access control which domains it can uh reach out to or specific MCP servers. So we'll do unrestricted. And then here you can see you can like choose packages you want to install um
etc. And then next we'll just go and start the session. All right. Now we're going to go test run our session. Uh right. Tell me jokes about cats.
And you'll see the model started thinking and it came up with a cat joke. Yeah. That that was like a that that's how easy it is for you to go build your
agent. Now this uh agent is up and running in the cloud. All you have to do is start a session with the agent, send events, get back events. Everything else is managed by us. So we're managing all
the infrastructure, the hosting, the harness, the sandbox management, the scaling, the storage of your event log. Um and then you can focus on building your product around this. Um thank you.
I'll now hand it over to Gabe to do the more hands-on bit of the workshop and hopefully hopefully we have better luck with Wi-Fi. Thank you. [applause]
Can you hear me? Great. I see there's a bunch of people that are kind of squished in the back. There's room here and here if you want to come in uh so we can fit everybody in. Um, before we
start, here's a repo that we're going to be using. Um, so if you want to take a picture of the QR code, get clone that repo. Um, cd into the research desk directory and then run uh npm install or
bonai. Um, I'll let you do that and then I'll walk you through through what we're going to be building today uh together. Um, still see some phones out. I know you're
there's Wi-Fi issues. Hopefully, you can you can get uh you can get to the repo. Um Okay. Oh. Oh, I see you. Okay. Cool. All right. So, what are we what are we
going to be building today? We're I'm going to show you how you can use cloud manage agents to build a variety of different applications. We're going to start with uh building a custom
chat-like interface so that you can talk to an agent. We can say hello. Um, then we're going to augment that agent into a multi- aent uh ecosystem so that it can delegate tasks uh to sub agents. We're
going to give it skills uh specifically a skill to be able to call um the SEC website and get SEC filings for individual tickers uh and analyze specific forms. Um then we're going to
be able to use the these new enhanced agents to run more asynchronous um agentic tasks that can take a long time to run. Um and then we're going to bring it all together by enabling the sort of
map reduce interface where we can talk to an agent and that agent can then delegate to many agents uh and do sort of like a map produce thing. um showing you how you can build a custom harness
on top of CMA to orchestrate you know your own work with custom tools uh and manage multiseession um kind of interactions. Uh and then we'll finish up by I'll show you uh our deployments
API that allows you to set up recurring work on a schedule so that you can run uh sessions uh on a on a given cadence to run kind of longer term analysis that keeps updating itself. Sound good?
quiet. Okay, great. Um, all right. So, let's switch over to um a coding interface. I'm going to be using cloud code for this. You're free to use whatever coding agent you prefer. Uh, I
don't think anybody's writing code by hand, so I'm not going to do that either. Um, there is a skill that we've loaded up in your repository that's going to walk you through the workshop
step by step. Um, so if you want to rerun the workshop after I'm done, uh, later on when you have better Wi-Fi and better internet access, uh, you're able to do so, um, at home. Um, so the first
thing you're going to want to do is I assume you've checked out the repository. You've cded into the research desk repo. You've run buni. We're going to spin up our uh, server
with uh, bun rundev. I'm going to make this bigger. I'm assuming nobody can see. Okay. Is that better? >> Yeah. And then if you have questions while Gabe Gabe is presenting, feel free
to raise your hand and I can run to you with the mic. >> We we have helpers. So if you run into issues, raise your hand. People will come um help you help you out. Um cool.
So if you've gone this far, you'll basically be able to load this website. Uh and the first thing you're going to want to do is go to the setup page. Um everybody with me so far?
>> Yeah. Okay. Um now we're gonna Yeah. Um, okay. Once uh once uh he takes a picture, we're we're going to start up uh our workshop skill. So in cloud code,
you can type in uh workshop start. Uh and it's going to start running through the workshop for you. We're going to do it in six acts. Uh I apologize there's no intermission in between. Um I hope
that's okay with y'all. Um but we're going to go through it. Uh Claude will uh sure we're going to go into auto mode. Um Claude Claude is going to keep track of
each of the acts so that you can continue on. You can keep typing workshop next so that it you know it's going to keep uh progressing through the through the thing. Uh I'm going to ask
Claude to do it for me but handhold handhold me and teach me what it's doing. Um and so the first thing it's going to basically make sure that our prerequisites are set. Um, it's going to
copy the m.example file into amp.local. Um, oh no, stop. We're not there yet. Okay, it's a little eager. Okay, it's going to copy that file. That file um has a few values for you to fill in.
You're going to want to paste in your API key. Um, you're going to want to paste in the SEC wants to know who you are when you're calling their API. So, you're going to have to put in your name
and email. Um, this is just so that they know who's calling them. Um, and then that's basically it. Once, um, once you've done all that, um, it's going to, well, we're going to move on to the to
the next thing. So, everybody with me so far, you've put in your API key into your.local, you put in your Edgar identifier. >> Show of hands,
>> be a quick show of hands. Or how many of you are able to follow along who might have Wi-Fi or >> Okay, we got one one person at least. So, [laughter]
MVP, pardon. Uh oh. Oh, for the repo. This one. >> Uh Rohan, you want to hand the mic? >> Yeah, thank you. >> Yeah, the slide with the credential.
That's it. The setup. >> This one. >> Thank you. >> Okay. Sure. And then and then given given people don't have Wi-Fi plea
please like stop us and ask questions so we can at least make this more interactive and help you understand concepts so that you can build with your own time after the workshop.
>> Okay. Um I'm yeah I have a >> yeah this is the app spinning up uh when you clone and cd into research desk and run bun run rundev. This is a website you will you will get.
>> Can I ask a question? >> Yeah, I don't see you but >> Oh, you're here. [laughter] Uh I'm wondering like you know so when you say cloud handle and manage the
agents where is this running? Is this running on AWS? >> Yeah, that's a good question. Um so we we we're going to run it in our own infrastructure. Um today we we have like
a onep offering uh that we run on but we also have cloud platform on AWS if you want to run on AWS using your AWS credits. What we also offer and maybe we can talk about this after this demo is
self-hosted sandboxes. So if you want to bring your own sandbox through uh different providers like Burcell or Modal or Daytona or a bunch of others, you can do that. Uh even you know
through Google cloud and and AWS if you want to run your sandbox in that uh environment, you can. Um okay, I'm going to continue with the demo. U the the first thing you the next thing you want
to do is create push this button which is going to create um your agent. So it's going to create two things for us. um an environment which Priyanka talked about is a template of what the sandbox
is going to look like. Um for this demo, we're going to give it access to a few websites that it can reach out to, mainly the SEC. We're going to add uh some Python packages so that it can
reach out to to the SEC with you know some predefined uh Python packages. Um it's also going to create uh an agent. So it's going to create this very basic agent. Right now it has no tools. Um, it
has uh just a prompt. Uh, well, it has the basic tools, but there's no skills. There's no sub agent coordination, nothing like that. Um, I'm going to tell Claude to
um continue and implement the interface with which we can talk to this agent in our application. Uh, like Bianca mentioned, the main uh way you communicate with a session is through an
event stream. So you listen onto that stream for all the events that the agent is pushing back down to you and you can use that uh an endpoint to push events back up. So really everything that
happens in the communication for these more chat-like applications happens on that stream. So we can see here what Claude is doing is it's uh implementing the section so that when I press enter
in my chat box, it's going to send a user message event with the content that I'm sending to that session. And then it's going to route the SSE stream back onto my browser from my back end. So
it's going to listen onto um the events that are coming in the stream connection to our API and then reream them directly back onto the front end so that I can render them in a nice way. Um and once
Cloud has done that, we should be able to go to this desk tab and just say hi to our agent. Um, and if everything worked hopefully great. Um, so we can see here
now we're we're talking to our agent. If you wanted to build a chat-like application, this is where you would stop. Um, which is great, but we want to do more. The whole point of cloud
managed agents is that you can do longunning asynchronous tasks. Um, not just for minutes, but hours, days, weeks, and even months. Um, so let's see what we we have to do next. Right. The
next thing that we're gonna want to do is um Oh, Claude is very eager today. Um, okay. The next thing is we we want to augment our agent, right? We want to give our agent a skill so it can call
the SEC. We want to use multi- aent coordination so that um our session can delegate work to sub agents that are more specialized and capable of doing some tasks. Um, and we want to basically
move up the ladder of intelligence. Um so let's do that um by saying workshop next. Um now what claude is going to do is it's going to implement the provisioning scheme to upgrade our agent
so that it uses uh more sub aents or any sub aents um is we're going to give it a skill a skill that lets it uh um talk to the SEC. We've described how to use the Python package. We've described that it
has to use the Edgar identity to call. Um, we've we've described all of that and we're going to give it a custom tool uh for later in the workshop. Um, that custom tool is what we're going to be
able to use to tell our head of research agent to spin off multiple sessions that we will coordinate uh for a sort of custom harness. So, this is what Claude is doing here. Um, it's updating its
progress, creating this custom tool called dispatch analysis. We're going to come back to this in a second. um we give it a schema. Um it's going to update the the description of the main
agent so that it creates a new version of that agent. Uh this new version will have um oh pager duty. Um uh this new version will have uh a few different sub aents uh that it can
delegate work to as well as a reference to itself. So if the main agent ever wants to spin off a copy of itself with its same prompt, it can do that. Um this is important because uh sometimes you
don't necessarily know what sort of tasks your session is going to want to do, but you have a general idea that you want to have sub agents that you know spin up and do work. Um so you can
define a self reference to you know yourself that clock can then spin up a sub agent for. Um important this question has come up in the past. It doesn't copy the transcript of that
session into the sub agent. It just copies over the tool description and the schemas uh and the prompt uh into into that sub agent. Um cool. So now if we go into here, we
should see that there's a new agent. Oh, I think I need to provision them. So there should be a button in our setup uh that says staff the desk. When I do that, it's actually going to run Oh.
Uh uh okay. I ran this before. Let me delete the skills so that I can recreate it for you. Um I was trying to do the magic uh cook show thing, but that's fine. Um okay. So we'll delete the
skill. We'll ask cloud to reprovision it. Um it's going to create all those resources and then we'll be able to see here all the sub aents, the skill that
we created, um a memory store. So for these sessions, we we we also support um longunning memory store that can be shared across sessions. Uh and we're going to use that so that different
sessions can learn about different financial instruments and they can share that knowledge uh with each other. And at the end of this workshop, I'll show you how you can use that memory
longunning memory to keep improving your agents long term over many many uh sessions, days, months, years. Um cool. So we should see here that the agent now in here
um has the skill that we described. It has sub agents uh with their own prompts and tools and permissions. Uh it has a prompt and it has a model. Um
and I think we're we're ready to use this new dedicated sub agent to do our first asynchronous longunning task. So let's uh tell Cloud to move on to the next thing. The next thing that we want
to do is um we want to tell Claude not in a chat-like application but a more asynchronous thing. Hey, research this financial instrument and give me a report in a format that I define and I
care about. And we're going to use something called outcomes. Outcomes is how we've created self-s supervision. So in an outcome, you tell Claude, "This is your task. This is your rubric to
evaluate your task. It's going to go off and do that work and then when it thinks it's completed, it's going to ask a sub agent to look at the rubric and the work that Claude did uh and evaluate did it
do it correctly and did it do it to completion. Uh if it does then great and if it doesn't it will try again. Um so here what Claude is implementing for us is basically this userdefined outcome
event. So that anytime we start a session in our UI that I'll show you for a specific ticker, it's going to send this userdefined outcome with a specific rubric uh max iterations um and a
description of the task at hand. And so then Cloud will go run that task for us. Um and so what does that look like in the UI? If you look to this scorecard, um I can tell Claude, hey, I I want to
analyze Nvidia. Oh, please. Okay, great. So, it's going to kick off a session to do the analysis on Nvidia. Um, this is going to take a while. I'm
going to do the cook show thing and show you a previous session um of of what happened. Uh, it's going to take about 10ish minutes. Um, but here's something I ran yesterday. Uh, the first thing
we're telling Claude is here is an outcome. I want you to analyze Nvidia's most recent uh SEC quarterly filings. Um you're going to use Edgar identities. This is where your Edgar identities are.
Um you have memory. So check if you've already done analysis previously and make sure that you use that instead of refiling and researching things that you already know. Um I want you to find the
most uh recent form that I care about and run the analysis over that. Um and then I want two deliverables. I want you to write a note in in my memory um for that specific company and I want you to
write a scorecard in a well- definfined structured output and I want you to save that in a specific file location. Um so then Claude went off to work started calling tools. It didn't find any notes
in its memory store. Um it uh looked in a few different places and then it decided okay I know what to do. I'm going to leverage my sub agents to do the bulk of this work. Um I'm going to
spin up a financial um expert analysis agent uh or extractor and then a risk evaluator analysis. And you can see all of that in the uh console here for full observability. Each of these lines here
represents a thread and a thread represents the work done by a sub agent. And so each of the sub aents went off to work and then when they were done they let the main agent know hey this is what
you asked of me uh and then the main agent woke up uh got that information from the sub agents and then uh filed everything it needed to do and created the deliverable. It created the JSON
file that you know we specified uh and it made sure to update the memory and then the cell supervision started. So it kicked off a sub agent to verify that the outcome that we defined is being
met. Um it looked at the rubric. It analyzed all the work that was done uh in the session and analyze the artifacts that it created. Uh and it decided in the end that this was satisfied that
this you know scorecard was delivered uh that the numbers are well defined uh that the memory update you know was correct um and everything looked good. Um I don't I think this is still running
but once that completes uh you would see a table here of each of those tickers uh because we can extract the report the scorecard from the session and download it. You put in a database for this demo
we put it locally. Um but I think you get the idea. Um okay this is great but what if you want to do a hundred or or 10 tickers at a time? Wouldn't it be great if I could
chat with an agent to build a custom harness that could then delegate work uh for each of the tickers into a different session and then when it's all done aggregate the results and give me uh a
bunch of scores. Um so that's that's what we're going to do next. Um we're going to leverage that tool that custom tool that we created to build our custom orchestration harness in our server. um
where when we talk to our main agent at some point that main agent is going to decide hey I want to spin up uh 10 different sessions to do work um we'll get that oh I skipped a step sorry um
for we're going to update the skill uh first u so I I wanted to show you that these skills are are live artifacts that you can update um so you can improve the skill uh in this case uh we're adding
the ability to look at a new form Uh and then when you do so any new sessions that the reference at skill will get that new information. Um so this is something that you can continuously
update. You can look at previous sessions to see how they performed. We're going to see that at the end of this uh demo that that happens with with something we call dreaming. Um but yeah
this this is what is happening. Um okay I'm going to move on to the orchestration bit. Um, so now that yeah, that basically had skills published. Um, I think if I go here, we should see
>> any questions so far. Um, I know this is a lot to take in. Uh, Travis, there's a question over here. >> Um, and I I know someone came up to me asking about like how long it takes to
get the credits. It should be only five minutes. Um, I know the form says one to two days, but you should see it within five minutes. >> Um, I was curious about the the data
access uh from the SEC that the the research agent is grabbing. Does that require some sort of credentials? And are you going to walk through the part of how the how that credential injection
or or brokering works? >> Yeah. So, for for the SEC, it just requires your email and your name. Um, for this demo, we just inject that in the prompt. But in a real production
application, we would use something called vaults. Uh the way vaults work is you define a secret uh that you want to you inject in an environment variable. And instead of putting the real secret
in, what we put is is a placeholder. Uh and then when Claude is doing work and it wants to use that environment variable, um it just injects the placeholder value. We at egress swap it
out for the real value. only on the host domains that you specified are allowed for that secret. So if you say uh my super secret MVAR can only be used for the SEC and then cloud tries to use it
for entropic.com we wouldn't inject we wouldn't do the swap but when it tries to call the sec.com it would do the swap for that >> is essentially like an API where you can
use it on console where you just go create a vault give it a name you'll get an ID and then you essentially include that ID in your session as a way for us to authenticate with your MCPS your CLI
you for example you could do like a Google Workspace CLI that you install on your um sandbox and then you can give us like your API key as your environment credential and again we'll we'll kind of
follow the same method that >> uh Gabe talked about. So that that allows us to >> essentially >> bots also work with ooth credentials
forps and they also work >> Gabe I don't know if you want to just show it on on console like the quickly like the vault creation. >> Yeah. Um so you you can create a vault
here and then in a vault you would bundle credentials. Um so you can have MCP O um maybe you do notion you know we have a few predefined things for OOTH things um so whenever you reference a
session you inject that vault ID and it will bundle all the credentials into your session and whenever it tries to use any of the domains that you specified in your credentials it will
swap for the real um credential instead of the placeholder. >> Yeah. Um, quick question. What part of the capabilities in the cloud agents SDK and what's about it?
>> Can you what what do you what do you mean? >> You have the agent SDK, right? I presumably this is built on top of the agent SDK. I want to know what part of
the capabilities are in the agent SDK layer and what's outside of it. >> So the the agent SDK is packaged for uh like more more it's a headless cloud code version. So it's actually we've
built a generic version of that. We've took taken a lot of inspiration about the benefits and lessons from the agent SDK, but we've rebuilt uh the harness to be a more generic not just coding
specific thing. >> So it's sort of a fork. >> Uh yeah. Yeah. It's basically like its own harness. Agent SDK is basically like a cloud code in the form of a binary
that you can take and run wherever you want. This is a fully hosted solution running in our cloud and we have our own harness that's heavily inspired by cloud codes harness and we frequently stay
stay in touch for like any enhancements across the board. >> Uh I I saw another hand in the in the back over there. >> We have a question here. Yeah,
>> couple more. >> Thank you. Uh I guess the question as you're orchestrating between multiple agents um is there some guard rails that those agents would not go into loops and
like consume a lot of tokens um as they're generating or working together? >> Um some of that we leave up to you to orchestrate and figure out. I mean we're if you're building these sort of custom
harnesses, there's nothing stopping you from using uh like other providers if you wanted to do a sort of multi- uh multi-provider system. Um, we are exploring the idea of what it means to
do like budget tighter budget controls that you can say use no more than this amount of money per session, use no more than this amount of tokens per session. Um, but that's not something we offer
today. >> Um, yeah, go ahead. >> Couple more hands. >> Question. Uh, earlier you were showing us in console kind of a transcript of
the manager and then the sub aents and you could kind of go through and see what they were doing. if you were to come across maybe uh a mistake or like it was going down the wrong path or you
wanted to change history. I mean obviously that's what's happened. Is there some way to correct it or to kind of go back to that point in time and kind of start over try it with some
additional guidance? Is there any anything like that in here? >> You can steer it. So I could I could uh I think here if I say send event I could send a new user uh message event and
steer the agent. Uh it'll queue it up. it's, you know, you can send that 10 messages at a time and it's fine. We don't currently have a way for like rewind and roll forward. Um, but that's
definitely something that uh if if people are interested, we we would look into >> Yeah, session forking is essentially what you're asking for. We we've had a
couple a few customers who have asked for it. That's it's it's on our road map. So hopefully we'll have it at some point. >> Thank you.
>> Uh yeah, I see a couple hands here if you want to hand. Yeah, thank you Rohan. Uh so >> where are you? Okay. Okay. Yeah. Go ahead. [laughter]
>> Assuming all of this is running in the cloud like like you know u you guys have any road map to get collective knowledge across these agents like let's say in this one particular application you have
10 users. >> Would would you mind speaking up? It's a little hard to hear. Oh, so so if you assuming all of this is running in the cloud and this application is being
distributed across tens and hundreds of users and all of them are using it for the purposes of like you know doing this financial research. Do you have I know you guys have session logs. Is there a
collective >> memory that you guys are looking into or even have a road map of? >> We have it today. We actually used it in this demo. So we use the a memory store
um that allows you to share knowledge across sessions. Uh and you can see here that what's happening is um the agents are each populating individual information about what they've done so
far. Um and then I'll show you at the end how you can consolidate that knowledge over time using dreaming so that you can continuously improve the memory stores as different agents run
into different edge cases and collectively learn from each other. So just to follow up on this so you're storing this in anthropics infrastructure. Say if you want to
delineate that and and have things running in AWS bedrock and I have my own like uh data clusters or something for sandboxing. Then you mentioned about self-hosting. How much of a lockin is
this piece versus like how flexible is it? So all of this API based so you you can extract the memories and put them in different systems if you want to. We don't currently support out of the box
other memory providers in the session but you would just build an MCP or a custom tool to do so. It's the way I would imagine doing it. And >> and also to add to the initial question
about like having user level org level memory we do allow for attaching multiple memory stores. So you can imagine having a user level memory store and then having some sort of orwide
memory store and then you bring both to each session. So you can start collecting user level as well as or level memories and then like Gabe said none of this is locked in. You can like
take your data and go anywhere else if you wanted to. Everything is an API call. >> Yeah. >> Um I had a quick question as you walk
through the agent workflow. I noticed that the grader is at the main agent level, right? like so how do you grade these sub aentic tasks that are happening and then how do you manage
memory across those as well >> right >> that's a good question so uh there's two dimensions to this sub aents in the way that we've built them are meant to uh
augment your context and leverage the capabilities of your of your session by collaborating between them uh the outcome definition happens at like an overall arching uh session level so you
define the outcome and you say this is what I want you to accomplish. The main agent then uses you know uses sub agents to accomplish that work. It collaborates between them and it eventually says uh
I'm done. Um there is no currently support for like a sub outcome definition. >> Quick followup like so as you have this dreaming feature right like so how do
you learn like if the sub agents have some sort of you know uh you know offc course sort of actions then how do you sort of learn from that? Yeah, uh today the dreaming is constrained to uh memory
stores. So I I'll show you that uh uh in a second, but basically the way dreaming works is it will analyze the transcripts and the memories created across multiple sessions uh so that it can update its
memories so that future agents know what to pull. Um but you can imagine that we can continue to expand that for skills and for prompts and for agent definitions so that you can you have
this continuous learning uh functionality. But right now we just have it for for memory stores. >> See a hand here. And then I'll I'll go back to
>> Hello. >> Yeah. >> Here. >> Where where is here? >> Here.
>> Oh, hi. >> Thank you. Uh if you have your let's say company data in either I don't know traditional store or rag or whatever and you want to expose it to this type of
agents through a custom CLI or MCP. what would be the safe path to put it in your environment? >> Yeah, that's a great question. There's two two ways to do this. If you are
using if you can access that uh via CLI with an API or via uh an MCP, uh you would just create an MCP, give it uh give it the authentication vault uh and it would just be able to call your MCP
directly. If you don't want to expose your MCP to the internet, we have a feature called MCP tunnels which lets you uh create a tunnel from your um network into ours so that you don't
expose it to the internet and we would do it that way. If if that's uh too big a barrier to climb, we do support self-hosted sandboxes so that you can bring your own sandbox which has which
is peered with your network and has access to your applications and services and the agent would just call the sandbox that you manage and host um and be be able to access your your resources
that way. >> Yeah. >> Um I had a question on dependencies for the environments. So I saw something you could load in dependencies. Um is that
at runtime or right before um it's created? It >> it's at creation time. Uh and then we'll cache it and reuse uh that environment across like re reinstantiations of the
sandbox. >> So can you specify like internal dependencies or do you have to pick mpm package? >> Right. right now uh they are all they
all have to be accessible via npm cargo uh pip whatever but you can upload files uh directly into um the session if or GitHub repos if you want to install them that way
>> okay and one more thing uh deployments I see a tab for that >> yeah yeah I'm going to get to it I'm going to continue to the with the demo and then we'll we'll do more questions
in a