Where Cursor Helped Me Move Faster
I used Cursor to write a batch configuration script for 40 switches and it generated correct-looking VLAN commands that used the wrong syntax for our specific Cisco IOS version. The code looked clean, the Netmiko session handling was sane, and the loop structure was better than the rough script I had in my notes. The problem was not Python. The problem was the CLI it confidently placed inside the Python.
In our manufacturing environment, I write small automation scripts for repetitive network work: adding VLANs, collecting interface descriptions, checking trunk state, pulling MAC tables during maintenance windows, and validating firewall objects after change control. Cursor 0.44 was immediately useful for that work. With Python 3.11 on Ubuntu 22.04, it generated argument parsing, logging, inventory loading, exception handling, and CSV output without needing ten rounds of prompting.
The speed difference was real. Development time for network automation scripts dropped by 45% using Cursor, but testing time stayed exactly the same. A script that normally took me 4 hours to draft took 2 hours and 12 minutes with Cursor helping on structure, retries, and cleanup. The validation window still took 90 minutes because switches, firewalls, and production risk do not care how fast the editor wrote the first draft.
Fast is not the same as ready.
Where Cursor shines is pattern repetition. If I already know the workflow, it can fill in a lot of boring but necessary code around it. I still have to own the commands, the device assumptions, and the rollback path. My opinion after three weeks is simple: Cursor is excellent for automation scaffolding, but I would never let it be the authority on network intent.
Why Version-Specific CLI Still Bites
My mistake was blunt: I trusted the generated commands without running them against a single test device first — the syntax error was consistent across all 40 devices in the first push. I had reviewed the Python and missed the device-specific command syntax because the command looked like something I had typed a hundred times. That false familiarity cost us time during a maintenance window.
The same issue shows up across vendors. Cisco IOS, IOS XE, NX-OS, FortiOS 7.4.3, PAN-OS, ArubaOS, and Junos all have small syntax differences that matter. Cursor can infer intent from nearby code, but it does not know the exact parser behavior of a specific switch image unless I give it that context and still verify the output. It may generate a command that belongs to a newer release, a different platform family, or a blog post written for lab gear.
What I didn’t expect was how convincing the wrong answer looked. There was no hesitation, no warning, no hint that the generated VLAN syntax might not match the Cisco IOS train we were running. The script connected properly, entered config mode, sent commands in order, and failed consistently. That kind of failure is dangerous because the surrounding automation feels professional.
The CLI was the weak link.
I now treat AI-generated commands as untrusted input. I compare them against vendor docs, saved configs, and known-good commands from our own devices. For network automation, I care less about whether the generated code is elegant and more about whether the CLI has already survived a real parser. My opinion here is firm: AI can write the envelope, but the command payload still belongs to the engineer.
How I Set Up Cursor With Useful Network Context
Cursor became much more useful after I stopped treating it like a generic chatbot and started treating the repo like documentation. I added sanitized examples of our device inventory, naming conventions, platform labels, and standard connection profiles. I kept secrets out, but I left enough structure for Cursor to understand how our environment is shaped.
Our automation repo now has clear folders for inventory, templates, tests, scripts, and archived command samples. I use Python 3.11 virtual environments on Ubuntu 22.04, pin core packages, and keep a short README that says which platforms are in scope. I also include comments beside platform identifiers such as cisco_ios, cisco_xe, fortinet, and arista_eos because vague inventory names cause vague generated code.
- Keep sanitized running-config snippets for common interface, VLAN, trunk, and routing patterns.
- Store known-good command examples by vendor, platform, and OS version.
- Use explicit inventory fields for device_type, os_version, site, role, and management VRF.
- Add tests that parse generated command lists before any live device session runs.
- Write prompts that ask for dry-run output before asking for configuration pushes.
Context beats clever prompting.
The biggest improvement came from giving Cursor examples of our preferred failure behavior. I want scripts to fail closed, log the hostname, stop on authentication issues, and never continue silently after a config-mode error. Once those patterns existed in the repo, Cursor copied them fairly well. My opinion is that Cursor rewards disciplined repositories and punishes messy ones.
Using Netmiko, Napalm, and Ansible Without Losing Control
For Netmiko, Cursor is strongest when I ask for connection handling, command batching, structured logging, and output capture. I do not ask it to invent final device commands from scratch anymore. I give it the exact command list or a template and ask it to build the transport, guardrails, and reporting around that list.
You may also find this useful: Check out our guide on Python Network Config Backup: Automating Multi-Vendor Device Snapshots for more practical tips.
from netmiko import ConnectHandler
from pathlib import Path
def push_commands(device, commands):
with ConnectHandler(**device) as conn:
conn.enable()
output = conn.send_config_set(commands, error_pattern=r"% Invalid|Error|Ambiguous")
if "% Invalid" in output or "Ambiguous" in output:
raise RuntimeError(f"{device['host']} rejected generated CLI")
Path("logs").mkdir(exist_ok=True)
Path(f"logs/{device['host']}.log").write_text(output, encoding="utf-8")
return output
For Napalm, I like Cursor for compare-and-commit workflows because the library already pushes me toward a safer operational model. Candidate config, diff review, commit, and rollback fit the way I want network changes to behave. When Cursor writes Napalm code, I still inspect how it handles merge versus replace and whether it discards config on failure.
Ansible is different. Cursor can generate tasks quickly, but YAML that looks valid can still hide weak idempotency, bad variable names, or module choices that do not match the installed collection version. I have seen it mix patterns from older and newer network collections in one playbook. That is not a small issue during a production change.
Pretty YAML can still hurt.
My best workflow is to use Cursor for the first pass, then force the toolchain to be the judge. Netmiko scripts run in dry-run mode first. Napalm diffs get reviewed by a second engineer. Ansible playbooks go through syntax checks, inventory limits, and one-device execution before broader runs. My opinion is that Cursor should sit inside the workflow, never above it.
Test AI-Generated Network Code Like Production Depends on It
I changed my testing discipline after the 40-switch mistake. Every generated network script now needs a lab target, a parser check, or a single-device proof before it touches a batch. If I cannot test against the same OS family and version, I do not pretend the change is validated.
For our environment, that means a small test ladder. First, I run unit checks against command rendering. Second, I run dry-run output and inspect the exact CLI. Third, I use one noncritical switch or firewall policy object. Fourth, I expand to a small site group. Only then do I run the full inventory. That process did not get shorter with Cursor, and I do not want it to.
I also ask Cursor to generate negative tests now. I want bad credentials, unreachable hosts, config rejection, timeout behavior, and partial failure reporting covered before the script is trusted. It is good at creating pytest scaffolding, especially when I provide one existing test file as a model. The uncomfortable part is that test generation feels slower than code generation, but that is exactly the point.
The test is the work.
Cursor has earned a place in my network automation workflow, but only as a fast assistant with no change authority. It saves time on Python structure, Netmiko boilerplate, Paramiko setup, report formatting, and repetitive Ansible patterns. It does not replace vendor validation, lab testing, peer review, or the engineer who understands why one manufacturing cell losing network access can stop a line. My opinion after using it daily is that Cursor is useful precisely when I keep it on a short leash.