Did you know that you can use Claude

Code for a lot more than just coding?

Thanks to Anthropic's Claude Agent SDK

within our own automations and

workflows, we can build Cloud Code right

in. It unlocks an insane number of

possibilities. And I know that's a lot

to unpack, but that's what I'm going to

do for you in this video. But take a

look at this. First, I have my Claude

code integrated directly in Telegram.

And behind the scenes, I have it working

on my Obsidian vault. This is my second

brain, my knowledge base where I do all

my note-taking and my YouTube scripting.

And so I can have Cloud Code help me

with that. And since it's Telegram, I've

got it on my phone as well. So take a

look at this. I'll send a message on my

phone. You'll see it on my computer as

well. And then we'll see Cloud Code in

action. So add a small bullet point list

of potential personal AI automations to

my AI automations you need now script.

Okay, cool. So I'll send this in.

There's the message. And then Cloud Code

gets right to work. You can see it

typing right here. So, I'll go over to

my Obsidian Vault, and in just a second,

we'll see a bullet point list pop up.

And boom, there we go. Potential

personal AI automations. And of course,

Cloud Code has responded back to us in

Telegram as well, even listing out the

tools that it used to find the file,

read it, and edit it, just like you

would see in the Cloud Code terminal or

the terminal for any AI coding

assistant. But I won't even stop there.

I also integrated Cloud Code directly in

Obsidian. And so we have this chat

interface using the co-pilot community

plugin and I can talk to my cloud code

right here as well. So I can send in a

request like add a couple more ideas to

the potential personal AI automations in

the same script that I edited in

Telegram. And so we'll see that right

now. And there we go. We got the travel

planning assistant. It added a few more

as well. Looking really, really good.

And so I'm able to manage my notes and

knowledge and everything with the help

of Cloud Code in these different

applications that I can even use on my

phone. It is such an incredible thing.

And this is all thanks to the fact that

Claude Code is really just a simple yet

powerful wrapper on top of Claude. In

the article where Anthropic released the

Cloud Agent SDK, they called it the

agent harness that powers Claude Code.

And they've actually been using cloud

code for a lot more than just coding for

a while now for things like deep

research, video creation, note

takingaking, like what I was doing with

Telegram and Obsidian. And so a couple

weeks ago, they renamed the Cloud Code

SDK to the Cloud Agent SDK because it

really is this engine that can power any

of our AI agents we want to build. And

that's what I'm going to show you how to

do in this video. I'll show you how I

did that Telegram and Obsidian

integration. I'll even show you how in

code like in our Python code here, we

can create our own custom AI coding

assistants to define our own system

prompts and build into our own

workflows, even build our own eval

monitoring solutions. Like I have a

Sentry integration that I'll show you

how to build where you can watch when we

execute our Cloud Code agents remotely.

We can see all the decisions that it

decided to make, things like our token

usage. There's just so much that I have

for you in this video. And so I'm going

to start by giving you just the basics

of the agent SDK. How do you build with

it? Then we'll get into some of these

integrations. I've got a lot in store

for you here. So with that, let's go

ahead and get started with the cloud

agent SDK. So I'll have a link in the

description to this repository where I

have all of my demos and integrations

for things like Telegram and Obsidian.

And a lot of what I have created here is

just based on the claude agent SDK

documentation which I will also have

linked in the description. And I will

say claude code is definitely the

agentic coding leader right now. A lot

of other tools are going to be copying

them and building their own SDKs. Like

we already have the codeex SDK which

just unfortunately doesn't have the best

documentation right now. But even if you

don't like cloud code for whatever

reason because there's rate limiting

issues or pricing or whatever, just know

that like what we're covering today is

definitely going to be possible in the

near future with a lot of different AI

coding assistants. And so going down in

their quick start here, we can see that

in just a few lines of Python code, we

can create our own custom instance of

cloud code. We can set our own MCP

servers and permission and system prompt

and then we can query it. And what

you're looking at right here is

essentially what I have in the first

quick start for you. So you can go

through this read me or just follow

along right now. In the quick start, I

have this simple query example.

Literally the most basic script I could

possibly create for you. So first we

define our options for our own agent,

which I'm only specifying my system

prompt, but if you look at the docs, you

can see all the different things that we

can change to really customize the agent

for us. And then I'm going to query just

by calling the query function that I'm

importing from the cloud agent SDK. Just

a simple Python package. And then I'm

going to loop through all of the

messages that it gives me back and print

those out to my terminal. And so the way

that this works is very similar to the

Claude Code CLI. So I'll pull that up as

a reference here. When we use the CLI

for any AI coding assistant, we send in

our request and we get these blocks

back. It doesn't stream the tokens out

to us in real time. It just sends us

text block by block like a little bit of

information recognizing our request.

Then it's going to do some web search.

So this is a block. And then it

acknowledges the web search. So that is

another block. And then it edits a

couple of files. Like each one of the

messages or actions that it takes is a

single block that we receive as the

user. And so it works the exact same

way. So when we loop over the messages

here, we're looping over those blocks of

information or action that cloud code is

doing based on the query that we send

in. And so let's see this in action.

I'll go back over to my terminal here

and I'll run the simple query example.

And in just a little bit, we're we'll

get a couple of blocks back starting

with the system prompt. And yep, so

there's our system prompt. And then

we'll see the response that we get from

coding assistant. So I asked it a very

simple question and we get a very simple

response. And then claude code with the

agent SDK also always sends a final

result message. So that's our signal

that we are done interacting with cloud

code for this turn in our code. And by

the way, the way that authentication

works with the cloud agent SDK is it

will default to using your enthropic API

key and your credits there if you have

it set as an environment variable. But

if you don't set your anthropic API key,

which honestly I'd recommend not doing

that, then you can use your cloud

subscription. So the exact same

authentication that you've gone through

already if you're using cloud code. So I

have my max plan. I'm not paying any API

credits for everything I'm showing you

in this video. I just had to run login,

which I've already done this because I'm

using cloud code on my desktop. Now, the

other quick start example I want to show

you is how we can build our own custom

CLI using the cloud agent SDK. So, it's

going to be pretty much what we do with

cloud code normally, but I'm showing you

that like now it's our own Python code,

our own custom agent. So, I have a

little bit of logic here for managing

the conversation history. Not super

important. The main thing that I want to

cover here is the primary chat loop. So

we define our options just like we saw

in the very basic example for the agent

SDK. So we have things like our current

working directory where we looking at

files. We've got our system prompt that

you can customize. And then also another

thing I wanted to show you is that we

can set up granular permission

management. The tools that we're

allowing our agent to use. And this is

also where you can set other things for

MCP servers. By the way, more on that in

a little bit. So yeah, we create our

options and then we create an instance

of our client. And then we just call

that query function again. So

client.query sending in the latest user

prompt. And then just like before, we

are looping through all of those message

blocks that we get when we are receiving

that response from the cloud agent SDK

client. We're printing out regular text

in one way and then the tool usage in

another way. So we can also see live in

the terminal the tools that cloud code

is deciding to use just like we'd see in

their official CLI. So there we go. It's

just cool that I can even say official

CLI. Like we're building our own CLI

right here. So I'm going to start this

here. Python simple CLI.py.

And it's not as pretty as the official

CLI. But the point here is just to show

you how easy it is to use cloud code

programmatically. So I can say hello for

example, just get a really simple

response back. And then I can also say

something like what files do I have in

my directory? So having it use probably

a bash tool here. So yep, there we go.

using the bash tool to check the files

in my directory and then in a second

we'll get our response back. There we

go. We have the following files sessions

simple cli.py simple query example. So

basically the directory that I'm giving

it access to right now if we look at the

settings is just the current directory

that we're in. So this quick start

folder right here. So it's just listing

out everything that we got here. And I

could go and make file changes here. I

can do everything I can do with cloud

code assuming I'm giving it the

necessary permissions. So that's a quick

start how we use cloud code

programmatically. Now I want to cover

with you the fun stuff. Let's actually

get into the integrations now with

things like Obsidian and Telegram and

see how powerful this really is cuz it's

cool to see us customize an agent in a

CLI. But at this point it's not anything

really special compared to what we've

already had. So now that's what I want

to show you. So the first integration I

have for you is the Obsidian one which

I'm just stoked for. I've been doing so

much with Obsidian recently. Actually

moving away from a lot of different

applications to it. If you're not

familiar with Obsidian, it is a

completely free and local solution for

knowledge management and note-taking.

It's kind of like my second brain. I'm

using this every single day now. A lot

of things that are similar to Notion,

probably the best comparison that I can

make that you're probably familiar with.

And so they also have a bunch of open

source community plugins. So you can

enable that and go to the co-pilot one

specifically. This is the one that I

installed that gives me this interface

that I can connect to really any LLM

that I want or my own custom agent.

That's what I'm going to be running

right here in the demo that I gave

earlier. And so you can follow the

instructions here to get everything

fully set up. I'm not going to cover

that in detail right now. Uh just a

couple of steps that you need to connect

your own custom agent once you're

running this API that I have around the

cloud agent SDK. So cool. Let me

actually run this right now. So, I'll go

back to my terminal here. I'm within the

Obsidian Integration folder. I'll just

run Python API server.py. Fall is

working. Well, then you have it running

on port 803.

And I'll show you the code for this

really quickly. Like, I don't want to

focus on code a ton in this video, but I

still want to show you specifically how

I'm using the Cloud Agent SDK in Python

code. I've even got an MCP server for

this one as well. It's pretty cool. So,

okay, let's go down to the chat

completions endpoint. So, I'm using

OpenAI API compatibility to create this

API endpoint in a way where I can

automatically hook into it with that

C-pilot extension that I have installed

in Obsidian. And so, we get the

conversation history that's sent in from

Obsidian. We format it in the way that

we need to send into the cloud agent

SDK. And then we define our options just

like we've been doing with the quick

starts here. So, I have my current

working directory, which this is going

to obviously be my Obsidian vault. I

have my system prompt, the allowed

tools, and then I also have an MCP

server connected here as well. So, this

isn't something I've shown yet, but the

same way that you set up MCP servers in

your coding assistance or cloud desktop

or whatever, you can set those up as

JSON config for your own custom cloud

agent SDK agents as well. Very, very

easy. This is a standard IO server that

I use quite a bit called sequential

thinking. It basically just gives

instructions to the coding assistant or

my agent in this case how to think

through something step by step. Um, so I

just get more thinking tokens out of my

LLM. And so I set up all my options,

pass it in, and then I create a client

just like we did in the other example,

call the query, and then I'm going to go

through all of the message blocks that

I've received. And I'm specifically

converting them into a format that I can

stream out to Obsidian so that I get the

output on in my Obsidian vault just in

the the right-h hand chat bar right

here. So yeah, pretty simple overall. I

mean, there's a good amount of code that

goes into the OpenAI converter here that

I don't need to cover. You can take a

look at that, throw this into your AI

coding assistant if you really want to

dive in. But the main thing I wanted to

show you is just how easy it is to use

our own agents and then set up all the

things that we want like permissions and

MCP servers and system prompts and

everything. It is a beautiful thing. So,

we can go back into Obsidian here. I'll

start a new conversation and I'll just

say my potential personal AI automation

list is too long. Make it shorter. So

really not providing much context at

all. So it's going to have to search and

find this list and it's going to have to

think about how to shrink it and then

finally edit the file to do so. So I'll

come back once it's done that. All

right, there we go. Much nicer. That was

getting way too long. And then we get

the response on the right hand side as

well. Now this isn't the most perfect

integration. I don't have this as a

production ready thing for you to use

right now. It doesn't print out the

blocks one by one and so it just kind of

throws everything to me at the end here.

But it's working great. It made the

edits. It was able to find my file and

know exactly what to change. Really,

really neat. So, that is the Obsidian

integration. And it's just cool to see

Claude Code doing something other than

coding. It's helping me with knowledge

management and YouTube scripting. It's

really, really neat. And the thing that

I'm taking advantage of here that Claude

Code gives me that other agents don't is

the really powerful capabilities around

searching through files, reading files,

and editing files. That's the agentic

wrapper on top of claude that I get to

leverage in my own code. Now, so this

entire video I've been focusing on

everything except for coding with the

Claude agent SDK. But honestly, probably

the most powerful use case is creating

our own custom AI coding assistance with

the tooling that we have here. And so

going back to the Telegram integration

now, before I was just using this to

work on my Obsidian Vault, but I'm going

to show you how with my phone now, I can

kick off remote Claude code tasks with

my own custom agent with all its own

configuration. And so this is the last

example that I have in the repository

for you. And I've got two examples

specifically. I've got one with the

Century integration and one without. So

there's two things that I want to show

you with this. First of all, I want to

show you how easy it is to build cla

code into any application we want. Like

I'm picking Telegram here, but this

could be Slack, it could be GitHub, it

could be email, whatever you want. And

then the other thing with Sentry I want

to show you is how when we have Cloud

Code built into our own workflows and

automations, we get to set up our own

monitoring solutions, which makes it

very easy for us to go back later,

especially when we're using remote

coding, and validate the work that's

done with our coding assistant. and we

can actually see the decisions that it

has decided to make. And so before I

execute this example, I want to show you

what that looks like really quick. So

I'm within my Sentry dashboard here and

they have documentation on how to

instrument your own AI agents with

Sentry, which is what I did to really

just get Claude code hooked into Sentry.

And then I have this amazing dashboard

that I showed you just really briefly

earlier where we have all these traces.

So every single time we interact with

Claude code, it's a single trace that we

can click into to see the response and

the prompt that went into it. We can

look at the different tools that it

executed. Like in this case, it decided

to um edit the bot.py file, for example.

We can see the tokens usage and the

number of tool calls for each execution,

how long it took. Like all this

visibility we get is very very

important. If you're actually serious

about building your own AI coding

assistance and your own AI coding

workflows, you need this kind of tracing

and that's what Sentry gives us. So very

very cool. So with that going into the

implementation here, it's pretty simple

overall. There's a lot of different

custom commands that I built into

Telegram. So that's neat. But the main

thing is I have this function to handle

any message that comes in through the

Telegram API that is sent to my bot. And

this could be a Slack bot. It could be a

Gmail account that I have. Like I just

need to have some kind of trigger to

watch for messages. And then I can pass

on the request to Claude code. So going

down here, I set up my options for the

Cloud Agent SDK just like I've been

doing in the past. And then I call it

right here. So, I get my client and I

query and then I go through all the

message blocks. I receive them and then

I'm going to send them all back out to

Telegram. Very easy. The code took me a

single shot to build with Claude code.

By the way, I did not iterate a single

time on building this entire bot for

Telegram and Claude Code. It was so so

cool. So, this is my bot. Now, here's

what I'm going to do. I have this

running in my terminal on my other

monitor already. And I'm going to change

my directory. So, one of the nifty

commands I have because this is my own

custom agent. I can do all these things.

I can do set current working directory.

I can go back into and copy this path

right here. And I'm going to paste it in

right here. Let me add a space and

delete this part. Okay. So, what I'm

doing with this command is I am changing

live where my remote cloud code is

pointing to on my computer. So now it's

ready to make changes in this repository

that we've been playing with today. And

so what I'm going to do, this is

actually so nuts. I'm going to from my

phone ask Claude Code to improve itself,

my own custom agent. I'm going to have

it edit its own file to add an MCP

server. And so back on my phone here,

I'm saying add the sequential thinking

MCP server to my Claude agent. And I'm

going to be editing this file that I

just showed you, the Telegram bot with

Sentry. And I'm also going to be telling

it to look at the previous example with

the Obsidian integration because that's

where I already connected the sequential

thinking. So, all right, I'll send this

in. Boom. And it's typing. So, it's

going away at the request already. And

it's going to build out my sequential

thinking MCP. So, in just a second here,

I'm going to actually go to the part

it's going to edit because it's going to

edit the options. So, in just a second,

we'll see it change this live. And the

request started from my phone. And it's

going and it's going. And there we go.

All right. We've added the sequential

thinking MCP and the permissions as

well. So, that doesn't have to ask me

when it wants to use any of the tools in

this server. That is just so so cool.

And yep, I see on my phone that I got

the response. So, let's go back to

Telegram. Awesome. So, we got all the

blocks sent to us at once. It's similar

to Obsidian where I probably could

refine this more, but yeah, it did a lot

of tool calls to accomplish this. So, it

read through this file a bunch with glob

and read and then finally it made that

edit at the end to update our

configuration. That is just so cool. And

so, now it literally fixed itself or I

should say improved itself. So, I can go

back to my terminal here. I can run it

again and then I can actually ask it

like use sequential thinking to give me

five fun facts about claude code. All

right. So, I'll go ahead and send this

in, and I'll come back once it comes

back with its request, cuz it does take

quite a while when it thinks through

something step by step. All right,

perfect. It took its sweet time, but

finally, we have our response. So, here

are our five fun facts. And then, yeah,

it used the sequential thinking many,

many times because every time it wants

to produce a thought, it calls the

server again. But yeah, this is freaking

awesome. We literally used our own cloud

agent SDK agent to improve itself. And I

didn't even have to do it right from my

computer. We can also go back into

Sentry here. I'll refresh and we can see

the logs for this so that if we weren't

watching the live, we can go back and

validate that everything actually makes

sense. So yeah, let me click into this

one here and we can see all of the tool

executions for sequential thinking. This

is I mean pretty much what we saw in

Telegram, but we can also dive in more

to see the different tool inputs for

example. So we can actually dig into the

parameters it sent to all the tool calls

for the sequential thinking MCP. So ton

of visibility, ton of customizability.

We can integrate with any application we

want like email, telegram, slack,

whatever. This is just a beautiful

playground. So that is everything that I

got for you on the claude agent SDK and

building our own custom agents. And I

really do think this is the future of AI

coding where we take this lightweight

wrapper like Claude code and we use it

to programmatically define our own

custom agents to build them into our

code bases, our workflows to really suit

our needs the best because when we use a

tool out of the box, we can never

customize it as much as we'd want

compared to when we actually get to

write the code. And I hope that I

enlighten you to that today. So, if you

appreciate this video and you're looking

forward to more things on AI agents and

AI coding, I would really appreciate a

like and a subscribe.