Building AI Agents for Telecom Network Operations

This article demonstrates how to build AI agents for telecom network operations that correlate alarms and perform triage using a portable, framework-agnostic skill approach.

• Agents reason about telecom alarms using structured domain knowledge packaged as reusable skills, not fine-tuned models
• Skills separate reasoning procedures (SKILL.md) from reference data (domain files), keeping prompts small while enabling on-demand access to thousands of lines of domain detail
• Cascade detection algorithm identifies root causes vs. symptoms by matching infrastructure keywords, sorting by timestamp, and surfacing the earliest event per site
• Three-layer architecture: Reasoning layer (triage procedures), Reference layer (domain files), and Retrieval layer (Amazon Bedrock Knowledge Base for long-tail lookups)
• Framework-agnostic design integrates with Strands Agents, LangChain/LangGraph, and Amazon Bedrock Agents without rewriting the skill
• Tested with 47 alarms: agent produced triage in ~20 seconds vs. 30+ minutes manual correlation by L1 engineer

Skills enable production-ready AI agents for telecom operations by combining procedural reasoning with structured domain knowledge, scaling across vendors and protocols without model retraining.