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 Stay 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 section of AI potentialities, 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 gained’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, however 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 meant.
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 study 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 it’s possible you’ll recall, the LLM (giant language mannequin) that powers AI methods is actually an algorithm skilled on huge quantities of information. An LLM can “perceive” your questions and directions. On its personal, nevertheless, the LLM is restricted to the info it was skilled on. It could actually’t even search the net for present film showtimes with out some “software” 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 software’s objective. However just lately, a brand new framework for constructing AI instruments has gotten loads of pleasure and is beginning to turn out to be a brand new “customary” for software growth.
This framework is named the Mannequin Context Protocol (MCP). Initially developed by Anthropic, the corporate behind Claude, any developer to make use of MCP to construct instruments, known 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; nevertheless, at the moment, MCP seems to be the method for software 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 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 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 is no such thing as a scarcity of AI instruments and platforms at the moment. There’s ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of a lot of them frequently for varied 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 need 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 information 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 study 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 potential choices able to go. The primary is Ollama, a robust open-source engine for working LLMs domestically, 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 shopper for working LLMs, however it isn’t an LLM itself. It gives entry to a lot of LLMs accessible for obtain and working. With so many LLM choices accessible, it may be overwhelming whenever you get began. The important thing issues for this weblog put up and demonstration are that you just want a mannequin that has been skilled for “software 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 thought for a software to construct. After some thought, I made a decision a great “hey world” for my new NetAI challenge could be a method 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 challenge. Along with being a library that I’m very accustomed to, it has the good thing about computerized output processing into JSON by means of 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-obligatory[str] = "ios",
) -> Non-obligatory[Dict[str, Any]]:
# Construction a dictionary for the system 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)
system = testbed.gadgets[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 realized it was frighteningly simple to transform my perform into an MCP Server/Instrument. I simply wanted so as to add 5 traces of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Howdy World")
@mcp.software()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Nicely.. it was ALMOST that simple. I did must make a number of changes to the above fundamentals to get it to run efficiently. You may see the full working copy of the code in my newly created NetAI-Studying challenge on GitHub.
As for these few changes, the modifications I made had been:
- A pleasant, detailed docstring for the perform behind the software. MCP purchasers use the main points from the docstring to know how and why to make use of the software.
- After some experimentation, I opted to make use of “http” transport for the MCP server somewhat than the default and extra widespread “STDIO.” The explanation I went this manner was to arrange for the following section of my experimentation, when my pyATS MCP server would probably run throughout the community lab atmosphere itself, somewhat 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 growth to get it working with out errors… however I’m doing this weblog put up “cooking present type,” 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 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” software. Whereas not “standardized, “most MCP Shoppers use a really widespread JSON configuration to outline the servers. LMStudio is one in every of these purchasers.
Wait… in the event 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 gadgets configured for direct SSH entry utilizing the PATty characteristic.
Let’s see it in motion!
Okay, I’m positive you might be 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 gadgets within the preliminary message.
I did this as a result of the pyATS software wants the tackle and credential data for the gadgets. Sooner or later I’d like to take a look at the MCP servers for various supply of fact choices like NetBox and Vault so it might probably “look them up” as wanted. However for now, we’ll begin easy.
First query: Let’s ask about software program model data.
You may see the main points of the software name by diving into the enter/output display screen.
That is fairly cool, however what precisely is occurring right here? Let’s stroll by means of the steps concerned.
- The LLM shopper begins and queries the configured MCP servers to find the instruments accessible.
- I ship a “immediate” to the LLM to contemplate.
- The LLM processes my prompts. It “considers” the totally different instruments accessible and in the event that they is likely to be related as a part of constructing a response to the immediate.
- The LLM determines that the “send_show_command” software is related to the immediate and builds a correct payload to name the software.
- The LLM invokes the software with the right arguments from the immediate.
- The MCP server processes the known 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 may do in the event you had been requested the identical query.
- You’ll take into account the query, “What software program model is router01 working?”
- You’d take into consideration the alternative ways you would get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d determine on a software and use it to assemble the data you wanted. Most likely SSH to the router and run “present model.”
- You’d evaluation 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 little bit about how these “AI Brokers” work below the hood.
How about yet another instance? Maybe one thing a bit extra advanced than merely “present model.” Let’s see if the NetAI agent can assist determine which swap port the host is linked to by describing the fundamental 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’s going to require the LLM to ship and course of present instructions from two totally different community gadgets. Similar to with the primary instance, I do NOT inform the LLM which command to run. I solely ask for the data I would like. There isn’t a “software” that is aware of the IOS instructions. That information is a part of the LLM’s coaching information.
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 in the event you scroll again as much as the CML community diagram, you’ll see that it accurately 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 software creation and experimentation as attention-grabbing as I’ve. And perhaps you’re beginning to see the probabilities in your personal day by day use. If you happen to’d wish to attempt a few of this out by yourself, you will discover every thing you want on my netai-learning GitHub challenge.
- The mcp-pyats code for the MCP Server. You’ll discover each the straightforward “hey world” instance and a extra developed work-in-progress software that I’m including further options to. Be happy to make use of both.
- The CML topology I used for this weblog put up. Although any community that’s SSH reachable will work.
- The mcp-server-config.json file that 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 software. These aren’t required for experimenting with NetAI use instances, however System Prompts could 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 may prevent a while:
First, not all LLMs that declare to be “skilled for software 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 “software 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 energetic session. Because of this in the event 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 concern, you’ll must 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 drawback. 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 recommendations for an LLM that works properly with community engineering information? Let me know within the feedback beneath. Speak to you all quickly!
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