Where Cascade Felt Different From Cursor
I gave Windsurf Cascade a single instruction to build a network device inventory script and it autonomously created 8 files, installed 3 packages, and modified my shell config. I was testing on Ubuntu 22.04 with Python 3.11 against a small lab set of FortiGate units running FortiOS 7.4.3, plus a few older switches that still expose SNMPv2c because manufacturing networks collect ugly compromises over time.
Cursor felt like a very fast coding partner inside my editor. Windsurf Cascade felt more like I had handed a task ticket to a junior automation engineer who could type faster than anyone on my team. That distinction matters. Cursor usually waited for me to drive the file structure and execution. Cascade inferred the workflow, created modules, wrote a README, added a config example, generated a requirements file, and tried to run the thing.
That is power with a blast radius.
My first wrong assumption was that Cascade would behave like a code completion tool with better context. It did not. It acted on the environment. That surprised me more than the code quality, because network engineers are trained to treat execution context as part of the change. A script that runs fine from my laptop can still be dangerous if it writes credentials to the wrong path, uses the wrong interpreter, or hits a production firewall with the wrong API rate.
My opinion after three weeks is simple: agentic execution is the real product, not prettier autocomplete.
How I Used Cascade For Network Automation
My test prompt was intentionally plain: build a network device inventory script that reads devices from YAML, connects over API or SNMP, normalizes hostname, model, serial number, firmware, and management IP, then exports CSV and JSON. Cascade produced a Python package layout, logging, validation, and a starter test file before I had finished my coffee.
The initial prototype time for network automation tools dropped from 2 hours to 25 minutes using Cascade, but review time stayed constant. That metric became the center of my evaluation. Cascade made the first version faster, but it did not remove the work I still had to do: confirm assumptions, inspect dependency choices, check error handling, test against real devices, and make sure the output matched how our asset process actually works.
Speed is not trust.
The generated workflow was close enough to be useful. It separated FortiGate API collection from SNMP collection, used environment variables for secrets, and wrote structured logs. I still rewrote the inventory schema because the first version treated every device like a firewall, which is exactly the kind of subtle assumption that creates bad CMDB data later.
# Python 3.11
from pathlib import Path
import yaml
def load_devices(path: str) -> list[dict]:
inventory_file = Path(path)
if not inventory_file.exists():
raise FileNotFoundError(f"Missing inventory file: {inventory_file}")
with inventory_file.open("r", encoding="utf-8") as handle:
data = yaml.safe_load(handle) or {}
return data.get("devices", [])What I didn’t expect was how much value came from asking Cascade to explain its own file choices after generation. The explanation exposed its mental model. When that model matched our environment, I kept the structure. When it did not, I deleted aggressively. In my shop, generated code earns its place the same way human code does.
Guardrails I Now Set Before The First Prompt
After the shell config incident, I stopped treating the prompt as the first control. The environment is the first control. Before I let Cascade touch a repo, I create a clean workspace, open only the project directory, and write a short constraints file that describes our actual network assumptions: no production IP ranges, no credential storage, no global package installs, and no changes outside the repo.
My mistake was letting Cascade automatically run pip install in my base Python environment instead of a venv — something a junior engineer would know not to do but the AI did not ask. That was not catastrophic, but it was clarifying. The tool optimized for task completion, not for our operational hygiene.
I felt that one immediately.
These are the guardrails I now use before any agentic coding session:
- Use a disposable Git branch with a clean diff before starting Cascade.
- Create a Python 3.11 virtual environment and activate it manually.
- Block production subnets in sample config files and tests.
- Keep secrets in a local ignored file, never in generated examples.
- Require a dry-run mode for anything that touches devices.
- Review shell, path, and package changes before reviewing application logic.
I also pin versions early. If the target is Ubuntu 22.04, Python 3.11, Netmiko 4.3.0, PyYAML 6.0.2, and FortiOS 7.4.3, I put those in the prompt and in the repo notes. Cascade performs better when the operational box is small. My opinion is that loose prompts create confident fiction.
Permission And Isolation Choices That Saved Time
In a manufacturing facility, network automation rarely lives in a perfect cloud-native sandbox. We have jump hosts, old management VLANs, maintenance windows, locked-down admin desktops, and devices that cannot be rebooted casually because a production line depends on them. That makes file permission and environment isolation more than developer preference.
You may also find this useful: Check out our guide on Python Network Config Backup: Automating Multi-Vendor Device Snapshots for more practical tips.
I now run agentic IDE experiments in a dedicated folder with narrow permissions, separate SSH config, and no inherited production credential helpers. On Windows admin workstations, I use WSL2 with Ubuntu 22.04 for repeatable Python 3.11 behavior. On Linux jump boxes, I avoid running the IDE directly and move reviewed scripts over only after they pass local checks.
Contain the tool first.
The biggest practical win was forcing generated tools to support offline fixtures. Cascade can write mock device responses quickly, so I ask for JSON fixtures from FortiOS 7.4.3 API output and SNMP walk samples before I let anything near a real firewall. That shifts review from guessing to comparing expected fields.
I do not want an agentic tool with broad access to my shell, my home directory, and my network at the same time. One of those is enough. My opinion is that agentic coding belongs behind the same least-privilege thinking we already apply to service accounts.
Choose The Right AI Coding Tool For Ops Teams
Windsurf, Cursor, and Claude Code all helped, but they fit different operational habits. Windsurf Cascade was best when I wanted a workflow created from one instruction. Cursor was better when I already knew the shape of the code and wanted tight editing help across existing files. Claude Code was strongest when I wanted terminal-centered refactoring, test repair, and direct interaction with a repo without living inside a full IDE.
For our network security work, I would not standardize on only one. My hybrid setup is Windsurf for greenfield automation prototypes, Cursor for careful editor-driven changes, and Claude Code for repository maintenance and scripted cleanup. That mix sounds messy until I compare it with how we already use Wireshark, FortiManager, Ansible, Python, and packet captures for different parts of the same job.
No single tool owns the workflow.
The honest comparison is that Windsurf feels the most ambitious and the easiest to over-trust. Cursor feels controlled, especially for engineers who want to stay close to every edit. Claude Code feels direct and operational, which maps well to how many infrastructure teams already think. The right choice depends less on model quality and more on how much autonomy your team is ready to supervise.
If I were rolling this out to my own team, I would start with non-production inventory, config linting, and report generation. I would avoid change execution until we had logging, approvals, dry-run output, and rollback behavior reviewed by humans who know the plant. My opinion is that agentic IDEs are ready for network engineering, but not for unsupervised network authority.
Further Reading: For more in-depth information, refer to the official Fortinet Documentation.