AI-Assisted SD-WAN Automation PoC

This is the public, lightweight proof-of-concept version of a private lab project.

The private lab connects an LLM tool workflow to Cisco Catalyst SD-WAN Manager, Cisco Modeling Labs, and AWS. This public version keeps the architecture, workflow, diagrams, and small representative snippets, but intentionally leaves out lab-specific code, credentials, live URLs, raw configs, and backup data.

Only public product names and generic architecture are described here. Do not publish internal Cisco documents, customer data, restricted screenshots, live lab identifiers, private configs, or generated artifacts.

The private lab code stays private. This repository is the safe public version.

What This Shows

• How an LLM can operate through MCP/OpenAPI tools instead of guessing from a prompt.

• How a FastAPI backend can act as the safety layer between the model and network APIs.

• How Cisco SD-WAN Manager, CML, Terraform, AWS, and CI/CD can fit into one automation story.

• How to design guardrails for mutation: explicit approval, environment gates, IPAM checks, redaction, postchecks, and human-readable reports.

This repo is intentionally small, but it is not only documentation. The sample code runs a public-safe version of the same flow: inventory -> IPAM -> branch plan -> approval gate -> postchecks -> LLM-ready JSON.

What The Private Lab Does

In the private lab, one LLM-friendly tool can:

1. Create a Cisco C8000V branch edge in Cisco Modeling Labs.

2. Attach it to simulated INET/MPLS transport links.

3. Prepare SD-WAN onboarding data.

4. Patch day0/bootstrap values.

5. Attach an SD-WAN config group.

6. Poll deployment tasks.

7. Run reachability, control-plane, BFD, alarm, and config-sync postchecks.

8. Return structured facts for the LLM to summarize in plain English.

Architecture

flowchart LR
    user["Operator"] --> llm["LLM client<br/>Claude Desktop / IDE / MCP client"]
    llm --> tools["MCP or OpenAPI tools"]
    tools --> api["FastAPI automation backend"]
    api --> policy["Guardrails<br/>approval gates<br/>redaction<br/>IPAM"]
    policy --> sdwan["Cisco SD-WAN Manager API"]
    policy --> cml["Cisco Modeling Labs API"]
    policy --> aws["AWS / Terraform lab infrastructure"]

Key Idea

The LLM does not directly configure routers.

LLM = chooses tools and writes the human report
MCP/OpenAPI = typed tool contract
FastAPI = validation and execution layer
Network APIs = source of truth

That separation keeps the demo practical. The model can be useful without being trusted with raw shell access or uncontrolled network changes.

MCP Flow

MCP is the bridge between the LLM and the automation code. In the private lab, an MCP-capable client can call tools like:

get_devices
create_and_onboard_edge
diagnose_edge

The tool returns structured JSON. The LLM turns that JSON into a readable operator report.

sequenceDiagram
    participant User as Operator
    participant LLM as LLM Client
    participant MCP as MCP Tool Server
    participant API as FastAPI / Automation Engine

    User->>LLM: Create a demo branch edge
    LLM->>MCP: create_and_onboard_edge(edge_label, dry_run=true)
    MCP->>API: Run deterministic workflow
    API->>API: Inventory, IPAM, guardrails, postchecks
    API->>MCP: Structured JSON
    MCP->>LLM: Tool result
    LLM->>User: Plain-English summary

The public example keeps the same shape but uses sample data instead of live SD-WAN/CML APIs.

The MCP server is created in mcp_server/sdwan_tools_example.py:

mcp = FastMCP("sdwan-netops-public-example")

@mcp.tool()
def create_and_onboard_edge(edge_label: str, approve: bool = False, dry_run: bool = True):
    return run_create_edge(edge_label, approve=approve, dry_run=dry_run)

Run it with:

python mcp_server\sdwan_tools_example.py

See mcp_server/README.md and mcp_server/mcp_config.example.json for the MCP client configuration example.

Repository Shape

.
|-- README.md
|-- .env.example
|-- backend/
|   |-- app.py
|   `-- automation_engine.py
|-- mcp_server/
|   |-- README.md
|   |-- mcp_config.example.json
|   |-- sdwan_tools_example.py
|   `-- tool_catalog.py
|-- scripts/
|   |-- install_dev.ps1
|   |-- install_dev.sh
|   |-- register_gitlab_runner_windows.ps1
|   |-- register_gitlab_runner_linux.sh
|   `-- print_tool_catalog.py
|-- tests/
|   `-- test_public_flow.py
|-- terraform/
|   `-- aws-connector-example.tf
|-- .github/
|   `-- workflows/
|       `-- ci.yml
|-- .gitlab-ci.yml
`-- docs/
    |-- architecture.md
    |-- code-highlights.md
    |-- mcp-flow.md
    `-- public-release-checklist.md

Example Tool Result

{
  "status": "pass_with_warnings",
  "device": "SITE_520-Edge1",
  "reachability": "reachable",
  "control_connections_up": 3,
  "bfd_sessions": {
    "up": 10,
    "total": 12
  },
  "config_group": "In Sync",
  "blocking_alarms": 0
}

The LLM can then explain the result like an operator:

The edge is onboarded and reachable. Control connections are up and the config
group is in sync. Two BFD sessions are still down, so data-plane connectivity
needs a follow-up check, but this is not blocking fabric onboarding.

CI/CD

This repo uses GitHub Actions, not GitLab CI. The workflow is in:

.github/workflows/ci.yml

It currently runs:

• dependency installation

• Python syntax checks

• unit tests for the public-safe automation flow

• MCP tool catalog smoke test

• FastAPI/OpenAPI operation validation

• Terraform formatting and validation

• final pipeline summary

The private lab has a larger CI/CD path, but this public repo keeps the checks small so they run without Cisco, AWS, VPN, or secrets.

Open the visual pipeline here:

GitHub repo -> Actions -> CI/CD -> latest run

The workflow is split into jobs with needs: so GitHub draws the graph:

plan -> install -> tests / MCP smoke / OpenAPI smoke
plan -> Terraform validate
all checks -> summary

More detail: docs/cicd-flow.md.

GitLab equivalent:

.gitlab-ci.yml

If this repo is mirrored or imported into GitLab, that file creates the same public-safe pipeline shape: plan, install, tests, MCP smoke, OpenAPI smoke, Terraform validate, and summary.

Self-hosted GitLab Runner setup is documented in docs/gitlab-runner.md. The repo includes registration helper scripts, but registration requires a GitLab project runner token.

Local Smoke Test

python -m venv .venv
. .\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python -m py_compile backend\app.py mcp_server\sdwan_tools_example.py
python -m unittest discover -s tests -t . -v
python scripts\print_tool_catalog.py
uvicorn backend.app:app --host 127.0.0.1 --port 8088

Try the public-safe API:

Invoke-RestMethod http://127.0.0.1:8088/api/health
Invoke-RestMethod http://127.0.0.1:8088/api/sdwan/devices

$body = @{ edge_label = "DEMO_AutomationSite"; dry_run = $true } | ConvertTo-Json
Invoke-RestMethod -Method Post `
  -Uri http://127.0.0.1:8088/api/sdwan/onboarding/by-label `
  -ContentType application/json `
  -Body $body

OpenAPI docs are available locally at:

http://127.0.0.1:8088/docs

Or use the local installer:

.\scripts\install_dev.ps1

Linux/macOS:

bash scripts/install_dev.sh

What Is Not Included

• live SD-WAN Manager URL

• CML controller URL

• credentials

• API keys

• Terraform state

• private keys

• raw bootstrap configs

• actual customer or internal documentation

• generated lab backups

CV-Friendly Summary

Built an AI-assisted SD-WAN automation PoC where an LLM uses MCP/OpenAPI tools to create, onboard, validate, and explain virtual SD-WAN branch edges across Cisco SD-WAN Manager, CML, Terraform-managed AWS infrastructure, and CI/CD guardrails.