Hey there, everybody, and welcome to the most recent installment of “Hank shares his AI journey.” 🙂 Synthetic Intelligence (AI) continues to be all the fashion, 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 answer, 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 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 programs 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 meant.
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 chances are you’ll recall, the LLM (giant language mannequin) that powers AI programs is basically an algorithm educated on huge quantities of information. An LLM can “perceive” your questions and directions. On its personal, nevertheless, the LLM is proscribed to the information it was educated on. It might probably’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 functions. Initially, the creation of those instruments was advert hoc and diversified relying on the developer, LLM, programming language, and the software’s objective. However lately, a brand new framework for constructing AI instruments has gotten a number of pleasure and is beginning to turn out to be a brand new “normal” for software 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; nevertheless, at present, 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 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 at the moment. There’s ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of lots of them frequently for numerous AI duties. Nonetheless, 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 major cause for this need was that I needed to make sure all of my AI interactions remained fully on my pc and inside my community. I knew I’d 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 programs for all of the testing, I nonetheless didn’t like the thought of leveraging cloud-based AI programs. 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 of 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 regionally, or no less than 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 provide it a strive for my experimentation.
LMStudio is a shopper for working LLMs, nevertheless it isn’t an LLM itself. It gives entry to numerous LLMs obtainable for obtain and working. With so many LLM choices obtainable, it may be overwhelming while you get began. The important thing issues for this weblog put up and demonstration are that you just want a mannequin that has been educated for “software 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 software to construct. After some thought, I made a decision a superb “whats up world” for my new NetAI challenge can be a approach 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 automated output processing into JSON via the library of parsers included in pyATS. I may 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: 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 perform into an MCP Server/Software. I simply wanted so as to add 5 strains of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Howdy World")
@mcp.software()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Properly.. it was ALMOST that straightforward. I did should 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 challenge on GitHub.
As for these few changes, the adjustments I made have been:
- A pleasant, detailed docstring for the perform behind the software. MCP shoppers use the main points from the docstring to grasp 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 rationale I went this fashion was to organize for the subsequent section of my experimentation, when my pyATS MCP server would doubtless run throughout the community lab setting 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 sincere, it took a few iterations in improvement to get it working with out errors… however I’m doing this weblog put up “cooking present fashion,” the place the boring work alongside the way in which is hidden. 😉
python netai-mcp-hello-world.py ╭─ FastMCP 2.0 ──────────────────────────────────────────────────────────────╮ │ │ │ _ __ ___ ______ __ __ _____________ ____ ____ │ │ _ __ ___ / ____/___ ______/ /_/ |/ / ____/ __ |___ / __ │ │ _ __ ___ / /_ / __ `/ ___/ __/ /|_/ / / / /_/ / ___/ / / / / / │ │ _ __ ___ / __/ / /_/ (__ ) /_/ / / / /___/ ____/ / __/_/ /_/ / │ │ _ __ ___ /_/ __,_/____/__/_/ /_/____/_/ /_____(_)____/ │ │ │ │ │ │ │ │ 🖥️ Server identify: 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: Utility 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 Consumer and hook up with the server to have entry to the brand new “send_show_command” software. Whereas not “standardized, “most MCP Purchasers use a really widespread JSON configuration to outline the servers. LMStudio is one in every of these shoppers.
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 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 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 hook up with my community units within the preliminary message.
I did this as a result of the pyATS software wants the tackle and credential data for the units. Sooner or later I’d like to have a look at the MCP servers for various supply of fact choices like NetBox and Vault so it may well “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 software name by diving into the enter/output display screen.
That is fairly cool, however what precisely is occurring right here? Let’s stroll via 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 think about.
- The LLM processes my prompts. It “considers” the completely different instruments obtainable 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 referred to as request from the LLM and returns the end result.
- 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 in the event you have been requested the identical query.
- You’ll contemplate the query, “What software program model is router01 working?”
- You’d take into consideration the other ways you might get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d resolve 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 slightly 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 can assist establish which change port the host is related to by describing the fundamental 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. Identical 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 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 in the event you 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 software creation and experimentation as attention-grabbing as I’ve. And perhaps you’re beginning to see the chances in your personal every day use. When you’d prefer to strive 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 straightforward “whats up 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 you can 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 circumstances, however System Prompts may 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 “educated for software use” will work with MCP servers and instruments. Or no less than those I’ve been constructing and testing. Particularly, I struggled with Llama 3.1 and Phi 4. Each appeared to point they have been “software customers,” however they didn’t name my instruments. At first, I believed this was on account 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 energetic session. Which means that in the event 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 situation, 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’s a bug filed with LMStudio on this drawback. 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 concerning the potential? Any recommendations for an LLM that works effectively with community engineering information? Let me know within the feedback beneath. Speak to you all quickly!
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