Automation Intermediate

Traditional network automation executes predefined scripts. AI-powered automation adds intelligence — the ability to analyze situations, make decisions, and adapt actions based on context. This lesson covers how to integrate AI into your automation workflows.

From Scripts to Intelligence

Level	Approach	Example
Level 0	Manual CLI commands	SSH into router, type `show interface`
Level 1	Script automation	Python script collects interface stats on schedule
Level 2	Event-driven automation	Webhook triggers remediation on alarm
Level 3	AI-assisted automation	ML model detects anomaly, suggests remediation
Level 4	Autonomous operations	System detects, diagnoses, and fixes issues automatically

Integrating ML Models with Ansible

You can call ML model predictions from Ansible playbooks to make intelligent automation decisions:

YAML

# AI-driven remediation playbook
- name: AI-Powered Network Remediation
  hosts: network_devices
  tasks:
    - name: Collect current metrics
      uri:
        url: "http://ml-api:8080/predict"
        method: POST
        body_format: json
        body:
          device: "{{ inventory_hostname }}"
          metric: "interface_utilization"
      register: prediction

    - name: Scale bandwidth if predicted overload
      ios_config:
        lines:
          - bandwidth 10000000
        parents: interface GigabitEthernet0/1
      when: prediction.json.overload_probability > 0.8

Intelligent Remediation Patterns

Predictive Scaling — AI predicts traffic spikes and pre-provisions bandwidth or spins up additional capacity
Anomaly-Triggered Diagnostics — When ML detects an anomaly, automation runs diagnostic commands and collects evidence
Self-Healing Loops — Closed-loop systems that detect issues, determine root cause, apply fix, and verify resolution
Configuration Validation — AI reviews proposed config changes against learned patterns to flag risky modifications

Safety First: Always implement guardrails in AI-driven automation. Start with "suggest and approve" before moving to fully autonomous actions. Include rollback mechanisms, rate limits, and blast radius controls.

Building an AI Automation Pipeline

Observe
Continuously collect telemetry from network devices via streaming telemetry or polling.
Analyze
Feed data through ML models for anomaly detection, prediction, and classification.
Decide
Apply business logic and confidence thresholds to determine if action is needed.
Act
Execute remediation via Ansible, Nornir, or API calls with appropriate safety checks.
Verify
Confirm the action resolved the issue and update the model with the outcome.

Next Step

Learn the best practices for deploying AI in production network environments.

Next: Best Practices →

← ML Models Best Practices →