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 prospects, 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 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, but it surely begins to work extra independently. Pushed by the objectives 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 facets 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 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 methods is actually an algorithm skilled 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 skilled on. It may well’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 purpose. However not too long ago, a brand new framework for constructing AI instruments has gotten a whole lot of pleasure and is beginning to grow to be a brand new “customary” for software improvement.
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 do not forget that we’re nonetheless within the very early days of AI and AgenticAI; nonetheless, at the moment, MCP seems to be the strategy for software 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 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 Study 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 a scarcity of AI instruments and platforms at this time. There’s ChatGPT, Claude, Mistral, Gemini, and so many extra. Certainly, I make the most of lots of them recurrently for varied 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 purpose for this need was that I needed to make sure all of my AI interactions remained solely on my laptop and inside my community. I knew I might 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 methods for all of the testing, I nonetheless didn’t like the concept 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 of native LLM work, and I had a few attainable choices able to go. The primary is Ollama, a robust open-source engine for working LLMs domestically, 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. Once I learn a latest weblog by LMStudio about MCP help now being included, I made a decision to present it a attempt for my experimentation.
LMStudio is a shopper for working LLMs, but it surely isn’t an LLM itself. It gives entry to a lot of LLMs out there for obtain and working. With so many LLM choices out there, it may be overwhelming if you get began. The important thing issues for this weblog publish 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 very good “whats up world” for my new NetAI challenge can 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 conversant in, it has the good thing about automated 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 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: Non-obligatory[str] = "ios",
) -> Non-obligatory[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 simple to transform my operate into an MCP Server/Instrument. I simply wanted so as to add 5 traces of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Hi there World")
@mcp.software()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Properly.. it was ALMOST that simple. 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 adjustments I made had been:
- A pleasant, detailed docstring for the operate behind the software. MCP shoppers 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 moderately than the default and extra frequent “STDIO.” The rationale I went this fashion was to organize for the following section of my experimentation, when my pyATS MCP server would probably run throughout the community lab atmosphere itself, moderately 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 publish “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 software startup. INFO: Utility startup full. INFO: Uvicorn working on http://127.0.0.1:8002 (Press CTRL+C to give up)
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” software. Whereas not “standardized, “most MCP Purchasers use a really frequent JSON configuration to outline the servers. LMStudio is one among 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 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 characteristic.
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 begin, 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 deal with 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 will probably “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 main points of the software name by diving into the enter/output display.
That is fairly cool, however what precisely is going on 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 out there.
- I ship a “immediate” to the LLM to contemplate.
- The LLM processes my prompts. It “considers” the completely different instruments out there 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” software is related to the immediate and builds a correct payload to name the software.
- The LLM invokes the software with the correct arguments from the immediate.
- The MCP server processes the known 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 would possibly do for those who 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 might get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d resolve on a software and use it to collect the data you wanted. In all probability 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 correct reply.
Hopefully, this helps demystify a little bit about how these “AI Brokers” work beneath the hood.
How about yet one more instance? Maybe one thing a bit extra complicated than merely “present model.” Let’s see if the NetAI agent will help determine 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 should always discover about this immediate is that it’ll 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 information.
Let’s see the way it does with this immediate:
And take 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 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 fascinating as I’ve. And possibly you’re beginning to see the probabilities in your personal day by day use. In the event you’d prefer to attempt a few of this out by yourself, you’ll find every thing you want on my netai-learning GitHub challenge.
- The mcp-pyats code for the MCP Server. You’ll discover each the easy “whats up world” instance and a extra developed work-in-progress software that I’m including further options to. Be at liberty 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 that you would be able to 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 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 would possibly 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 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 believed 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 lively session. Which means 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 difficulty, 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 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 concerning the potential? Any solutions for an LLM that works nicely with community engineering information? Let me know within the feedback beneath. Discuss to you all quickly!
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