Hey there, everybody, and welcome to the newest 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 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 realize 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, 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 features 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 you could recall, the LLM (massive language mannequin) that powers AI methods is basically 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 information it was skilled on. It might probably’t even search the online for present film showtimes with out some “software” 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 various relying on the developer, LLM, programming language, and the software’s purpose. However lately, a brand new framework for constructing AI instruments has gotten plenty of pleasure and is beginning to change into a brand new “normal” 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; nonetheless, at the moment, MCP seems to be the strategy for software constructing. So I figured I’d dig in and determine how MCP works by constructing my very own very primary 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 supply some instance code for creating an MCP server. I used to be able to discover extra alone.
Creating an area NetAI playground lab
There is no such thing as a scarcity of AI instruments and platforms at present. 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 major motive for this need was that I needed to make sure all of my AI interactions remained totally on my laptop and inside my community. I knew I might be experimenting in a wholly 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 danger 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 attainable choices able to go. The primary is Ollama, a strong open-source engine for operating 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. After I learn a latest 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 operating LLMs, however it isn’t an LLM itself. It offers entry to a lot of LLMs obtainable for obtain and operating. With so many LLM choices obtainable, it may be overwhelming whenever you get began. The important thing issues for this weblog submit and demonstration are that you simply 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 concept for a software to construct. After some thought, I made a decision an excellent “howdy world” for my new NetAI mission can be a approach for AI to ship and course of “present instructions” from a community machine. I selected pyATS to be my NetDevOps library of alternative for this mission. Along with being a library that I’m very aware of, it has the advantage of 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 primary Python operate 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: Non-compulsory[str] = "ios",
) -> Non-compulsory[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 discovered it was frighteningly simple to transform my operate into an MCP Server/Software. I simply wanted so as to add 5 strains of code.
from fastmcp import FastMCP
mcp = FastMCP("NetAI Whats up World")
@mcp.software()
def send_show_command()
.
.
if __name__ == "__main__":
mcp.run()
Effectively.. 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 may see the full working copy of the code in my newly created NetAI-Studying mission on GitHub.
As for these few changes, the adjustments I made have been:
- A pleasant, detailed docstring for the operate behind the software. MCP purchasers use the small print 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 reasonably than the default and extra frequent “STDIO.” The rationale I went this fashion was to arrange for the following section of my experimentation, when my pyATS MCP server would probably run throughout the community lab atmosphere itself, reasonably 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 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 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 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” software. Whereas not “standardized, “most MCP Shoppers use a really frequent JSON configuration to outline the servers. LMStudio is one in all 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 characteristic.
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 gadgets within the preliminary message.
I did this as a result of the pyATS software wants the deal with 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 may 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 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 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 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 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 totally different from what you may do should you have been requested the identical query.
- You’ll think about the query, “What software program model is router01 operating?”
- You’d take into consideration the alternative ways you can get the data wanted to reply the query. Your “instruments,” so to talk.
- You’d determine on a software and use it to collect the data you wanted. Most likely SSH to the router and run “present model.”
- You’d evaluate the returned output from the command.
- You’d then reply to whoever requested you the query with the right reply.
Hopefully, this helps demystify just a little about how these “AI Brokers” work underneath 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 help establish 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’ll 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 want. There isn’t a “software” that is aware of the IOS instructions. That data 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 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 software creation and experimentation as fascinating as I’ve. And possibly you’re beginning to see the probabilities to your personal each day use. If you happen to’d wish to strive a few of this out by yourself, you’ll find all the pieces you want on my netai-learning GitHub mission.
- The mcp-pyats code for the MCP Server. You’ll discover each the straightforward “howdy world” instance and a extra developed work-in-progress software that I’m including extra 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 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 software. These aren’t required for experimenting with NetAI use circumstances, however System Prompts will 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 “skilled for software 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 have been “software customers,” however they didn’t 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 energetic session. Which means 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 challenge, you’ll have to 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 fascinating to share.
Within the meantime, how are you experimenting with agentic AI? Are you excited concerning the potential? Any strategies for an LLM that works properly with community engineering data? Let me know within the feedback under. Discuss to you all quickly!
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