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 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 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 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, nevertheless it begins to work extra independently. Pushed by the targets we set, and using entry to instruments and techniques 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 features 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 could recall, the LLM (giant language mannequin) that powers AI techniques 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 restricted to the info it was educated on. It may’t even search the online 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 diverse relying on the developer, LLM, programming language, and the instrument’s purpose. However lately, a brand new framework for constructing AI instruments has gotten numerous pleasure and is beginning to grow to be a brand new “commonplace” for instrument 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, 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; nonetheless, presently, MCP seems to be the strategy for instrument constructing. So I figured I’d dig in and work out how MCP works by constructing my very own very primary 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 matters, and Kareem was useful sufficient to supply 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 as we speak. There may be ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of lots of them repeatedly 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 cause for this want was that I wished to make sure all of my AI interactions remained totally on my pc and inside my community. I knew I’d be experimenting in a completely new space of improvement. 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 techniques for all of the testing, I nonetheless didn’t like the thought of leveraging cloud-based AI techniques. I’d 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 potential choices able to go. The primary is Ollama, a strong open-source engine for operating LLMs regionally, or at the 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 offer it a attempt for my experimentation.
LMStudio is a shopper for operating LLMs, nevertheless it isn’t an LLM itself. It supplies entry to a lot of LLMs obtainable for obtain and operating. With so many LLM choices obtainable, it may be overwhelming once you get began. The important thing issues for this weblog publish 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 following factor I wanted for my experimentation was an preliminary thought for a instrument to construct. After some thought, I made a decision a great “howdy world” for my new NetAI challenge can be a means 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 acquainted with, it has the advantage of computerized output processing into JSON by way of the library of parsers included in pyATS. I might additionally, inside simply a few minutes, generate a primary Python operate 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: Optionally available[str] = "ios",
) -> Optionally available[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 realized it was frighteningly straightforward to transform my operate into an MCP Server/Software. I simply wanted so as to add 5 traces of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Hey World")
@mcp.instrument()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Effectively.. it was ALMOST that straightforward. I did must make just a few 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 have been:
- A pleasant, detailed docstring for the operate behind the instrument. MCP shoppers use the small print from the docstring to grasp how and why to make use of the instrument.
- 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 organize for the following part of my experimentation, when my pyATS MCP server would probably run throughout the community lab surroundings 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 improvement to get it working with out errors… however I’m doing this weblog publish “cooking present fashion,” 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 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 connect with the server to have entry to the brand new “send_show_command” instrument. Whereas not “standardized, “most MCP Purchasers use a really widespread JSON configuration to outline the servers. LMStudio is one in all these shoppers.
Wait… for those who’re questioning, ‘Wright here’s the community, Hank? What system are you sending the ‘present instructions’ to?’ No worries, my inquisitive pal: 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 instrument wants the deal with and credential info 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 could possibly “look them up” as wanted. However for now, we’ll begin easy.
First query: Let’s ask about software program model information.
You may see the small print 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 way of the steps concerned.
- The LLM shopper begins and queries the configured MCP servers to find the instruments obtainable.
- I ship a “immediate” to the LLM to contemplate.
- The LLM processes my prompts. It “considers” the totally different instruments obtainable 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” 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 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 for those who have been requested the identical query.
- You’ll take into account the query, “What software program model is router01 operating?”
- You’d take into consideration the alternative ways you may get the knowledge wanted to reply the query. Your “instruments,” so to talk.
- You’d resolve on a instrument and use it to assemble the knowledge you wanted. Most likely SSH to the router and run “present model.”
- You’d overview 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 underneath the hood.
How about another instance? Maybe one thing a bit extra complicated than merely “present model.” Let’s see if the NetAI agent can assist determine which change 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 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 units. Similar to with the primary instance, I do NOT inform the LLM which command to run. I solely ask for the knowledge I want. There isn’t a “instrument” 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 have a look at that, it was in a position to 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 for those who scroll again as much as the CML community diagram, you’ll see that it appropriately identifies interface Ethernet0/2 because the change port to which the host was related.
So what’s subsequent, Hank?
Hopefully, you discovered this exploration of agentic AI instrument creation and experimentation as fascinating as I’ve. And possibly you’re beginning to see the probabilities in your personal every day use. In the event you’d prefer to attempt a few of this out by yourself, you will discover all the things you want on my netai-learning GitHub challenge.
- The mcp-pyats code for the MCP Server. You’ll discover each the easy “howdy world” instance and a extra developed work-in-progress instrument that I’m including extra options to. Be happy to make use of both.
- The CML topology I used for this weblog publish. Although any community that’s SSH reachable will work.
- The mcp-server-config.json file which you can reference for configuring LMStudio
- A “System Immediate Library” the place I’ve included the System Prompts for each a primary “Mr. Packets” community assistant and the agentic AI instrument. 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 “educated for instrument use” will work with MCP servers and instruments. Or at the least those I’ve been constructing and testing. Particularly, I struggled with Llama 3.1 and Phi 4. Each appeared to point they have been “instrument customers,” however they did not name my instruments. At first, I assumed this was attributable to 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. Which means that for those who 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 must 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 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 fascinating to share.
Within the meantime, how are you experimenting with agentic AI? Are you excited in regards to the potential? Any ideas for an LLM that works properly with community engineering information? Let me know within the feedback under. Speak to you all quickly!
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