Hey there, everybody, and welcome to the most recent installment of “Hank shares his AI journey.” 🙂 Synthetic Intelligence (AI) continues to be all the fad, and getting back from Cisco Dwell in San Diego, I used to be excited to dive into the world of agentic AI.
With bulletins like Cisco’s personal agentic AI resolution, AI Canvas, in addition to discussions with companions and different engineers about this subsequent part of AI potentialities, my curiosity was piqued: What does this all imply for us community engineers? Furthermore, how can we begin to experiment and study agentic AI?
I started my exploration of the subject of agentic AI, studying and watching a variety of content material to realize a deeper understanding of the topic. I received’t delve into an in depth definition on this weblog, however listed below are the fundamentals of how I give it some thought:
Agentic AI is a imaginative and prescient for a world the place AI doesn’t simply reply questions we ask, nevertheless it begins to work extra independently. Pushed by the objectives we set, and using entry to instruments and methods we offer, an agentic AI resolution can monitor the present state of the community and take actions to make sure our community operates precisely as supposed.
Sounds fairly darn futuristic, proper? Let’s dive into the technical elements of the way it works—roll up your sleeves, get into the lab, and let’s be taught some new issues.
What are AI “instruments?”
The very first thing I needed to discover and higher perceive was the idea of “instruments” inside this agentic framework. As you could recall, the LLM (giant language mannequin) that powers AI methods is actually an algorithm skilled on huge quantities of knowledge. An LLM can “perceive” your questions and directions. On its personal, nonetheless, the LLM is proscribed to the information it was skilled on. It could possibly’t even search the net for present film showtimes with out some “device” permitting it to carry out an online search.
From the very early days of the GenAI buzz, builders have been constructing and including “instruments” into AI purposes. Initially, the creation of those instruments was advert hoc and different relying on the developer, LLM, programming language, and the device’s aim. However just lately, a brand new framework for constructing AI instruments has gotten plenty of pleasure and is beginning to turn out to be a brand new “commonplace” for device improvement.
This framework is called the Mannequin Context Protocol (MCP). Initially developed by Anthropic, the corporate behind Claude, any developer to make use of MCP to construct instruments, referred to as “MCP Servers,” and any AI platform can act as an “MCP Consumer” to make use of these instruments. It’s important to keep in mind that we’re nonetheless within the very early days of AI and AgenticAI; nonetheless, at present, MCP seems to be the method for device constructing. So I figured I’d dig in and work out how MCP works by constructing my very own very fundamental NetAI Agent.
I’m removed from the primary networking engineer to wish to dive into this area, so I began by studying a few very useful weblog posts by my buddy Kareem Iskander, Head of Technical Advocacy in Be taught with Cisco.
These gave me a jumpstart on the important thing matters, and Kareem was useful sufficient to supply some instance code for creating an MCP server. I used to be able to discover extra by myself.
Creating an area NetAI playground lab
There is no such thing as a scarcity of AI instruments and platforms right now. There’s ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of lots of them recurrently for numerous AI duties. Nevertheless, for experimenting with agentic AI and AI instruments, I needed one thing that was 100% native and didn’t depend on a cloud-connected service.
A main motive for this need was that I needed to make sure all of my AI interactions remained totally on my pc and inside my community. I knew I might be experimenting in a wholly new space of improvement. I used to be additionally going to ship knowledge about “my community” to the LLM for processing. And whereas I’ll be utilizing non-production lab methods for all of the testing, I nonetheless didn’t like the thought of leveraging cloud-based AI methods. I might really feel freer to be taught and make errors if I knew the danger was low. Sure, low… Nothing is totally risk-free.
Fortunately, this wasn’t the primary time I thought-about native LLM work, and I had a few attainable choices able to go. The primary is Ollama, a strong open-source engine for operating LLMs regionally, or at the very least by yourself server. The second is LMStudio, and whereas not itself open supply, it has an open supply basis, and it’s free to make use of for each private and “at work” experimentation with AI fashions. After I learn a current weblog by LMStudio about MCP assist now being included, I made a decision to present it a attempt for my experimentation.
LMStudio is a consumer for operating LLMs, nevertheless it isn’t an LLM itself. It gives entry to numerous LLMs accessible for obtain and operating. With so many LLM choices accessible, it may be overwhelming if you get began. The important thing issues for this weblog submit and demonstration are that you simply want a mannequin that has been skilled for “device use.” Not all fashions are. And moreover, not all “tool-using” fashions truly work with instruments. For this demonstration, I’m utilizing the google/gemma-2-9b mannequin. It’s an “open mannequin” constructed utilizing the identical analysis and tooling behind Gemini.
The following factor I wanted for my experimentation was an preliminary concept for a device to construct. After some thought, I made a decision a superb “good day world” for my new NetAI venture could be a manner for AI to ship and course of “present instructions” from a community system. I selected pyATS to be my NetDevOps library of alternative for this venture. Along with being a library that I’m very accustomed to, it has the good thing about computerized output processing into JSON via the library of parsers included in pyATS. I might additionally, inside simply a few minutes, generate a fundamental Python perform to ship a present command to a community system and return the output as a place to begin.
Right here’s that code:
def send_show_command(
command: str,
device_name: str,
username: str,
password: str,
ip_address: str,
ssh_port: int = 22,
network_os: Non-compulsory[str] = "ios",
) -> Non-compulsory[Dict[str, Any]]:
# Construction a dictionary for the system configuration that may be loaded by PyATS
device_dict = {
"units": {
device_name: {
"os": network_os,
"credentials": {
"default": {"username": username, "password": password}
},
"connections": {
"ssh": {"protocol": "ssh", "ip": ip_address, "port": ssh_port}
},
}
}
}
testbed = load(device_dict)
system = testbed.units[device_name]
system.join()
output = system.parse(command)
system.disconnect()
return output
Between Kareem’s weblog posts and the getting-started information for FastMCP 2.0, I discovered it was frighteningly straightforward to transform my perform into an MCP Server/Software. I simply wanted so as to add 5 traces of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Hiya World")
@mcp.device()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Properly.. it was ALMOST that straightforward. I did need to make a number of changes to the above fundamentals to get it to run efficiently. You’ll be able to see the full working copy of the code in my newly created NetAI-Studying venture on GitHub.
As for these few changes, the modifications I made had been:
- A pleasant, detailed docstring for the perform behind the device. MCP purchasers use the small print from the docstring to grasp how and why to make use of the device.
- After some experimentation, I opted to make use of “http” transport for the MCP server relatively than the default and extra frequent “STDIO.” The rationale I went this manner was to arrange for the following part of my experimentation, when my pyATS MCP server would seemingly run inside the community lab setting itself, relatively than on my laptop computer. STDIO requires the MCP Consumer and Server to run on the identical host system.
So I fired up the MCP Server, hoping that there wouldn’t be any errors. (Okay, to be trustworthy, it took a few iterations in improvement to get it working with out errors… however I’m doing this weblog submit “cooking present model,” the place the boring work alongside the way in which is hidden. 😉
python netai-mcp-hello-world.py ╭─ FastMCP 2.0 ──────────────────────────────────────────────────────────────╮ │ │ │ _ __ ___ ______ __ __ _____________ ____ ____ │ │ _ __ ___ / ____/___ ______/ /_/ |/ / ____/ __ |___ / __ │ │ _ __ ___ / /_ / __ `/ ___/ __/ /|_/ / / / /_/ / ___/ / / / / / │ │ _ __ ___ / __/ / /_/ (__ ) /_/ / / / /___/ ____/ / __/_/ /_/ / │ │ _ __ ___ /_/ __,_/____/__/_/ /_/____/_/ /_____(_)____/ │ │ │ │ │ │ │ │ 🖥️ Server title: FastMCP │ │ 📦 Transport: Streamable-HTTP │ │ 🔗 Server URL: http://127.0.0.1:8002/mcp/ │ │ │ │ 📚 Docs: https://gofastmcp.com │ │ 🚀 Deploy: https://fastmcp.cloud │ │ │ │ 🏎️ FastMCP model: 2.10.5 │ │ 🤝 MCP model: 1.11.0 │ │ │ ╰────────────────────────────────────────────────────────────────────────────╯ [07/18/25 14:03:53] INFO Beginning MCP server 'FastMCP' with transport 'http' on http://127.0.0.1:8002/mcp/server.py:1448 INFO: Began server course of [63417] INFO: Ready for software startup. INFO: Utility startup full. INFO: Uvicorn operating on http://127.0.0.1:8002 (Press CTRL+C to stop)
The following step was to configure LMStudio to behave because the MCP Consumer and hook up with the server to have entry to the brand new “send_show_command” device. Whereas not “standardized, “most MCP Shoppers use a really frequent JSON configuration to outline the servers. LMStudio is certainly one of these purchasers.
Wait… should you’re questioning, ‘Wright here’s the community, Hank? What system are you sending the ‘present instructions’ to?’ No worries, my inquisitive good friend: I created a quite simple Cisco Modeling Labs (CML) topology with a few IOL units configured for direct SSH entry utilizing the PATty function.
Let’s see it in motion!
Okay, I’m positive you’re able to see it in motion. I do know I positive was as I used to be constructing it. So let’s do it!
To start out, I instructed the LLM on how to connect with my community units within the preliminary message.
I did this as a result of the pyATS device wants the tackle and credential data for the units. Sooner or later I’d like to take a look at the MCP servers for various supply of reality choices like NetBox and Vault so it will possibly “look them up” as wanted. However for now, we’ll begin easy.
First query: Let’s ask about software program model information.
You’ll be able to see the small print of the device name by diving into the enter/output display screen.
That is fairly cool, however what precisely is going on right here? Let’s stroll via the steps concerned.
- The LLM consumer begins and queries the configured MCP servers to find the instruments accessible.
- I ship a “immediate” to the LLM to think about.
- The LLM processes my prompts. It “considers” the completely different instruments accessible and in the event that they could be related as a part of constructing a response to the immediate.
- The LLM determines that the “send_show_command” device is related to the immediate and builds a correct payload to name the device.
- The LLM invokes the device with the right arguments from the immediate.
- The MCP server processes the referred to as request from the LLM and returns the outcome.
- The LLM takes the returned outcomes, together with the unique immediate/query as the brand new enter to make use of to generate the response.
- The LLM generates and returns a response to the question.
This isn’t all that completely different from what you may do should you had been requested the identical query.
- You’ll think about the query, “What software program model is router01 operating?”
- You’d take into consideration the alternative ways you might get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d determine on a device and use it to assemble the data you wanted. Most likely SSH to the router and run “present model.”
- You’d evaluate the returned output from the command.
- You’d then reply to whoever requested you the query with the right reply.
Hopefully, this helps demystify a bit about how these “AI Brokers” work below the hood.
How about another instance? Maybe one thing a bit extra advanced than merely “present model.” Let’s see if the NetAI agent may help establish which swap port the host is related to by describing the essential course of concerned.
Right here’s the query—sorry, immediate, that I undergo the LLM:
What we should always discover about this immediate is that it’s going to require the LLM to ship and course of present instructions from two completely different community units. Similar to with the primary instance, I do NOT inform the LLM which command to run. I solely ask for the data I want. There isn’t a “device” that is aware of the IOS instructions. That information is a part of the LLM’s coaching knowledge.
Let’s see the way it does with this immediate:
And have a look at that, it was capable of deal with the multi-step process to reply my query. The LLM even defined what instructions it was going to run, and the way it was going to make use of the output. And should you scroll again as much as the CML community diagram, you’ll see that it appropriately identifies interface Ethernet0/2 because the swap port to which the host was related.
So what’s subsequent, Hank?
Hopefully, you discovered this exploration of agentic AI device creation and experimentation as attention-grabbing as I’ve. And possibly you’re beginning to see the chances in your personal every day use. When you’d prefer to attempt a few of this out by yourself, you will discover every little thing you want on my netai-learning GitHub venture.
- The mcp-pyats code for the MCP Server. You’ll discover each the easy “good day world” instance and a extra developed work-in-progress device that I’m including extra options to. Be at liberty to make use of both.
- The CML topology I used for this weblog submit. Although any community that’s SSH reachable will work.
- The mcp-server-config.json file which you could reference for configuring LMStudio
- A “System Immediate Library” the place I’ve included the System Prompts for each a fundamental “Mr. Packets” community assistant and the agentic AI device. These aren’t required for experimenting with NetAI use instances, however System Prompts will be helpful to make sure the outcomes you’re after with LLM.
A few “gotchas” I needed to share that I encountered throughout this studying course of, which I hope may prevent a while:
First, not all LLMs that declare to be “skilled for device use” will work with MCP servers and instruments. Or at the very least those I’ve been constructing and testing. Particularly, I struggled with Llama 3.1 and Phi 4. Each appeared to point they had been “device customers,” however they did not name my instruments. At first, I assumed this was resulting from my code, however as soon as I switched to Gemma 2, they labored instantly. (I additionally examined with Qwen3 and had good outcomes.)
Second, when you add the MCP Server to LMStudio’s “mcp.json” configuration file, LMStudio initiates a connection and maintains an lively session. Because of this should you cease and restart the MCP server code, the session is damaged, providing you with an error in LMStudio in your subsequent immediate submission. To repair this challenge, you’ll have to both shut and restart LMStudio or edit the “mcp.json” file to delete the server, reserve it, after which re-add it. (There’s a bug filed with LMStudio on this downside. Hopefully, they’ll repair it in an upcoming launch, however for now, it does make improvement a bit annoying.)
As for me, I’ll proceed exploring the idea of NetAI and the way AI brokers and instruments could make our lives as community engineers extra productive. I’ll be again right here with my subsequent weblog as soon as I’ve one thing new and attention-grabbing to share.
Within the meantime, how are you experimenting with agentic AI? Are you excited in regards to the potential? Any strategies for an LLM that works properly with community engineering information? Let me know within the feedback under. Discuss to you all quickly!
Join Cisco U. | Be a part of the Cisco Studying Community right now at no cost.
Be taught with Cisco
X | Threads | Fb | LinkedIn | Instagram | YouTube
Use #CiscoU and #CiscoCert to hitch the dialog.
Share: