README_AGENTS.md

Network Infrastructure AI Assistant

This project implements an AI-powered network infrastructure assistant with specialized port recommendation capabilities using the OpenAI Agents SDK.

Architecture Overview

The system follows a modular architecture based on the OpenAI Agents SDK:

Core Components

1. retriever_tool.py - Network information retrieval tool

   • Uses FAISS vector database for semantic search
   • Searches through network documentation and device configurations
   • Returns relevant network information with similarity scores

2. port_recommnedations.py - Specialized port recommendations agent

   • Expert agent focused on port/interface recommendations
   • Understands MLAG configurations and redundancy requirements
   • Provides specific device names and port numbers

3. app.py - Main orchestrator application

   • Combines retrieval tool and port recommendations agent
   • Provides Gradio web interface
   • Routes queries to appropriate tools based on context

4. port_recommendations_standalone.py - Standalone port recommendations

   • Direct access to port recommendations agent
   • Useful for testing and scripting

Key Features

Port Recommendations

• Automatic redundancy across MLAG pairs (leaf01/leaf02, leaf03/leaf04, etc.)
• Same port numbers across paired devices when possible
• Support for single port requests (without redundancy)
• Detailed responses with device names and specific port identifiers

Network Information Retrieval

• Semantic search through network documentation
• Device-specific configuration lookup
• Fabric-wide information queries

Usage Examples

Port Recommendations

# Various ways to request ports
"I need an unused port"                                    # Returns 2 ports with redundancy
"I need an unused port without redundancy"                 # Returns 1 port
"I need to dual connect a server to the network"          # Returns MLAG pair
"What ports are available on leaf01?"                     # Device-specific query

General Network Queries

"What is the BGP configuration?"
"Show me the VLAN settings"
"What's the loopback pool configuration?"

Running the System

Web Interface

python app.py

This launches a Gradio web interface where you can ask questions about the network infrastructure.

Standalone Port Recommendations

python port_recommendations_standalone.py

This runs a test suite with various port recommendation queries.

Testing Individual Components

from port_recommnedations import port_recommendations_agent
from retriever_tool import retrieve_network_information

# Test retrieval tool
result = retrieve_network_information("unused ports")

# Test port agent (requires async)
import asyncio
from agents import Runner

async def test():
    result = await Runner.run(port_recommendations_agent, "I need a port")
    print(result.final_output)

asyncio.run(test())

File Structure

agent-sdk/
├── retriever_tool.py                 # Network information retrieval
├── port_recommnedations.py           # Specialized port agent
├── app.py                            # Main orchestrator with Gradio UI
├── port_recommendations_standalone.py # Standalone port recommendations
├── faiss_index/                      # Vector database
├── prompts.yaml                      # Prompt templates
└── README_AGENTS.md                  # This file

Dependencies

• openai-agents: OpenAI Agents SDK
• langchain-community: FAISS and embeddings
• sentence-transformers: Text embeddings
• gradio: Web interface
• PyYAML: Configuration files

Agent Design Principles

Based on the OpenAI Agents SDK documentation:

1. Function Tools: The retriever uses @function_tool decorator for automatic tool setup
2. Agents as Tools: Port recommendations agent is used as a tool in the main orchestrator
3. Specialized Instructions: Each agent has domain-specific instructions and behaviors
4. Tool Routing: Main agent routes queries to appropriate specialized tools

Port Recommendation Rules

The port recommendations agent follows these key rules:

1. Default Redundancy: Always recommend two ports across different devices unless specifically requested otherwise
2. MLAG Pairing: Recommend ports across MLAG pairs (odd/even leaf switches)
3. Port Alignment: Try to use the same port number across paired devices
4. Specific Responses: Include device names and exact port identifiers
5. Query First: Will return only data from the leaf switches

Future Enhancements

• Add more specialized agents (security policies, VLAN management, etc.)
• Implement caching for frequently requested information
• Add support for configuration changes and validation
• Integrate with network management systems