AI for Network Config Validation & Deployment : Gemini Powered Network Automation Agent

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Welcome back to the NetworkEvolution learning series! Today, we are taking our Generative AI and Python skills to the next level by building an Agentic AI workflow specifically designed for network engineers. It is a prototype which demonstrates how to validate the configuration using LLM before pushing to the device.

We are building a automated pipeline that sits between the engineer and the network device. By passing proposed configuration snippets to an LLM (Google Gemini ), our agent evaluates the exact impact of the command.

How the AI Gatekeeper Works:

Intelligent Command Analysis: We feed the proposed configuration to Gemini alongside a strict prompt that establishes the AI as a "Network Engineering Expert."

Structured JSON Output: Instead of unpredictable text responses, we force the LLM to reply using strict data schemas. The agent classifies every change as either a DangerousConfig (which triggers an immediate block) or a StandardChange (which is approved for deployment).

Risk Mitigation in Real-Time: If a user tries to send a highly disruptive command—like no router bgp 100—the AI detects that removing the BGP routing process will disrupt peering sessions and cause massive connectivity loss. The execution is instantly blocked.

Automated Safe Deployments: When a standard, low-risk change is detected (like adding a loopback interface or updating an interface description), the agent generates a high confidence score and automatically pushes the configuration using the Netmiko library.

Code Architecture & Tools Used:
Throughout this video, we break down the code step-by-step, starting from a terminal-based Python application and moving into a fully functional web UI using Streamlit.

LLM Integration: We use Google Gemini to process the natural language and technical syntax of Cisco IOS commands. We control the execution flow using confidence threshold values.

Device Interaction: We utilize netmiko (version 4.6.0) and its ConnectHandler to establish SSH connections to our DevNet Sandbox devices and execute the approved commands via send_config_set.

Inventory Management: To keep the logic streamlined, we parse device credentials and hostnames from a local inventory.json file.

Streamlit Web App: We transition our backend logic into a beautiful Streamlit dashboard where users can select target devices from a dropdown, input their config snippets, and click "Analyze and Auto Deploy" while a visual spinner tracks the AI's real-time analysis.

Important Disclaimer: While AI is incredibly powerful, always remember that Large Language Models can produce false positives. Do not test dangerous configurations in a production environment blindly, as an incorrect AI approval could execute an outage-inducing command!

If this video helped you understand the power of Agentic AI in networking, please hit the LIKE button and SUBSCRIBE to NetworkEvolution for more advanced network automation tutorials!

What AI agent should we build next? Let me know in the comments below!
This video covers advanced network automation concepts, bridging the gap between artificial intelligence and traditional network engineering. By utilizing the Google Gemini API for configuration analysis, network administrators can build autonomous gatekeeper agents. Topics include Python programming for network engineers, Netmiko SSH automation, Streamlit UI development for networking apps, parsing JSON inventory files, Pydantic data schemas for LLM structured output, prompt engineering for networking topics, protecting Cisco routers from bad configuration changes, BGP configuration management, loopback interface deployment, and evaluating network risk and confidence scoring using Generative AI. This is essential viewing for network engineers looking to implement AIOps, intent-based networking, and secure, programmatic device management using modern DevNet practices.

Видео AI for Network Config Validation & Deployment : Gemini Powered Network Automation Agent канала NetworkEvolution