Hey there, everybody, and welcome to the most recent installment of “Hank shares his AI journey.” 🙂 Synthetic Intelligence (AI) continues to be all the trend, and getting back from Cisco Reside in San Diego, I used to be excited to dive into the world of agentic AI.
With bulletins like Cisco’s personal agentic AI answer, AI Canvas, in addition to discussions with companions and different engineers about this subsequent part of AI prospects, my curiosity was piqued: What does this all imply for us community engineers? Furthermore, how can we begin to experiment and find out about agentic AI?
I started my exploration of the subject of agentic AI, studying and watching a variety of content material to achieve a deeper understanding of the topic. I received’t delve into an in depth definition on this weblog, however listed here 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, but it surely begins to work extra independently. Pushed by the targets we set, and using entry to instruments and methods we offer, an agentic AI answer 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 points 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 wished to discover and higher perceive was the idea of “instruments” inside this agentic framework. As you might recall, the LLM (massive language mannequin) that powers AI methods is actually an algorithm educated on huge quantities of information. An LLM can “perceive” your questions and directions. On its personal, nonetheless, the LLM is proscribed to the info it was educated on. It may’t even search the net for present film showtimes with out some “instrument” permitting it to carry out an internet search.
From the very early days of the GenAI buzz, builders have been constructing and including “instruments” into AI functions. Initially, the creation of those instruments was advert hoc and diversified relying on the developer, LLM, programming language, and the instrument’s aim. However lately, a brand new framework for constructing AI instruments has gotten loads of pleasure and is beginning to turn into a brand new “commonplace” for instrument growth.
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 Shopper” 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, presently, MCP seems to be the strategy for instrument constructing. So I figured I’d dig in and determine how MCP works by constructing my very own very fundamental NetAI Agent.
I’m removed from the primary networking engineer to need 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 subjects, and Kareem was useful sufficient to offer some instance code for creating an MCP server. I used to be able to discover extra alone.
Creating a neighborhood NetAI playground lab
There isn’t any scarcity of AI instruments and platforms immediately. There may be ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of a lot of them usually for numerous AI duties. Nevertheless, for experimenting with agentic AI and AI instruments, I wished one thing that was 100% native and didn’t depend on a cloud-connected service.
A major purpose for this want was that I wished to make sure all of my AI interactions remained fully on my pc and inside my community. I knew I might be experimenting in a completely new space of growth. 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 chance 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 doable choices able to go. The primary is Ollama, a robust open-source engine for working LLMs regionally, or a minimum of 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. Once I learn a current weblog by LMStudio about MCP help now being included, I made a decision to provide it a strive for my experimentation.
LMStudio is a consumer for working LLMs, but it surely isn’t an LLM itself. It supplies entry to numerous LLMs accessible for obtain and working. With so many LLM choices accessible, it may be overwhelming while you get began. The important thing issues for this weblog submit and demonstration are that you just want a mannequin that has been educated for “instrument use.” Not all fashions are. And moreover, not all “tool-using” fashions really 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 subsequent factor I wanted for my experimentation was an preliminary concept for a instrument to construct. After some thought, I made a decision a superb “hey world” for my new NetAI mission can be a method for AI to ship and course of “present instructions” from a community machine. I selected pyATS to be my NetDevOps library of selection for this mission. Along with being a library that I’m very acquainted with, it has the good thing about automated output processing into JSON by 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 machine 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: Optionally available[str] = "ios",
) -> Optionally available[Dict[str, Any]]:
# Construction a dictionary for the machine configuration that may be loaded by PyATS
device_dict = {
"gadgets": {
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)
machine = testbed.gadgets[device_name]
machine.join()
output = machine.parse(command)
machine.disconnect()
return output
Between Kareem’s weblog posts and the getting-started information for FastMCP 2.0, I realized it was frighteningly simple to transform my perform into an MCP Server/Device. I simply wanted so as to add 5 traces of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Hi there World")
@mcp.instrument()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Properly.. it was ALMOST that simple. I did need to make a couple 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 mission on GitHub.
As for these few changes, the modifications I made had been:
- A pleasant, detailed docstring for the perform behind the instrument. MCP purchasers use the main points from the docstring to know how and why to make use of the instrument.
- After some experimentation, I opted to make use of “http” transport for the MCP server slightly than the default and extra frequent “STDIO.” The explanation I went this manner was to arrange for the following part of my experimentation, when my pyATS MCP server would probably run inside the community lab surroundings itself, slightly than on my laptop computer. STDIO requires the MCP Shopper 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 growth to get it working with out errors… however I’m doing this weblog submit “cooking present model,” the place the boring work alongside the best way 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 utility startup. INFO: Software startup full. INFO: Uvicorn working on http://127.0.0.1:8002 (Press CTRL+C to stop)
The subsequent step was to configure LMStudio to behave because the MCP Shopper and connect with the server to have entry to the brand new “send_show_command” instrument. Whereas not “standardized, “most MCP Purchasers use a really frequent JSON configuration to outline the servers. LMStudio is considered one of these purchasers.
Wait… should you’re questioning, ‘Wright here’s the community, Hank? What machine are you sending the ‘present instructions’ to?’ No worries, my inquisitive buddy: I created a quite simple Cisco Modeling Labs (CML) topology with a few IOL gadgets configured for direct SSH entry utilizing the PATty function.
Let’s see it in motion!
Okay, I’m certain you might be able to see it in motion. I do know I certain 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 gadgets within the preliminary message.
I did this as a result of the pyATS instrument wants the handle and credential data for the gadgets. Sooner or later I’d like to have a look at the MCP servers for various supply of reality choices like NetBox and Vault so it could actually “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 main points of the instrument name by diving into the enter/output display screen.
That is fairly cool, however what precisely is occurring right here? Let’s stroll by 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 totally different instruments accessible and in the event that they may be related as a part of constructing a response to the immediate.
- The LLM determines that the “send_show_command” instrument is related to the immediate and builds a correct payload to name the instrument.
- The LLM invokes the instrument with the right arguments from the immediate.
- The MCP server processes the referred to as request from the LLM and returns the consequence.
- 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 totally different from what you would possibly do should you had been requested the identical query.
- You’d take into account the query, “What software program model is router01 working?”
- You’d take into consideration the other ways you could possibly get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d resolve on a instrument and use it to collect the data you wanted. In all probability SSH to the router and run “present model.”
- You’d assessment 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 of about how these “AI Brokers” work below the hood.
How about yet another instance? Maybe one thing a bit extra complicated than merely “present model.” Let’s see if the NetAI agent may also help establish which swap port the host is linked to by describing the essential course of concerned.
Right here’s the query—sorry, immediate, that I undergo the LLM:
What we must always discover about this immediate is that it’ll require the LLM to ship and course of present instructions from two totally different community gadgets. Identical 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 “instrument” that is aware of the IOS instructions. That data is a part of the LLM’s coaching knowledge.
Let’s see the way it does with this immediate:
And take 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 linked.
So what’s subsequent, Hank?
Hopefully, you discovered this exploration of agentic AI instrument creation and experimentation as attention-grabbing as I’ve. And possibly you’re beginning to see the probabilities in your personal each day use. When you’d prefer to strive a few of this out by yourself, you could find the whole lot you want on my netai-learning GitHub mission.
- The mcp-pyats code for the MCP Server. You’ll discover each the straightforward “hey world” instance and a extra developed work-in-progress instrument that I’m including further options to. Be happy 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 instrument. 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 wished to share that I encountered throughout this studying course of, which I hope would possibly prevent a while:
First, not all LLMs that declare to be “educated for instrument use” will work with MCP servers and instruments. Or a minimum of those I’ve been constructing and testing. Particularly, I struggled with Llama 3.1 and Phi 4. Each appeared to point they had been “instrument customers,” however they did not name my instruments. At first, I assumed this was as a result of 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. Which means that should you cease and restart the MCP server code, the session is damaged, supplying you with an error in LMStudio in your subsequent immediate submission. To repair this difficulty, you’ll have to both shut and restart LMStudio or edit the “mcp.json” file to delete the server, put it aside, after which re-add it. (There may be a bug filed with LMStudio on this downside. Hopefully, they’ll repair it in an upcoming launch, however for now, it does make growth 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 options for an LLM that works effectively with community engineering data? Let me know within the feedback under. Speak to you all quickly!
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