Idea
Autonomous diagnostic platform improving root cause analysis in telecom and datacenter infrastructures to reduce downtime and operational costs.
Research Paper
Core Innovation
This paper introduces an agentic diagnostic framework where a Large Language Model autonomously navigates infrastructure models using a constrained toolset via the Model Context Protocol. Unlike traditional hard-coded or rule-based methods, it structures step-wise investigation ensuring grounding, reproducibility, and safe handling of ambiguous data.
Why It Matters
Telecom and datacenter operators struggle with complex failure propagation that impacts multiple customers and services. This platform reduces manual effort and maintenance costs by automating root cause analysis, enabling faster incident resolution and proactive risk mitigation. It scales across large infrastructures, improving reliability and customer satisfaction.
Market Size (TAM)
$20–50B TAM for telecom and datacenter infrastructure management; $5–10B SAM from large telecom and cloud operators. Driven by increasing infrastructure complexity and demand for automation.
Potential Customers & Pain Points
- Telecom operators – Complex failure diagnosis
- Datacenter operators – High incident resolution costs
- Cloud service providers – Risk mitigation for planned changes
Business Model
Subscription-based SaaS platform with tiered pricing based on infrastructure scale and feature set; potential for enterprise consulting and integration services.
Competitive Landscape
- Moogsoft
- BigPanda
- PagerDuty
- Splunk
Implementation Challenges
- Integration complexity with diverse legacy infrastructure models
- Trust and adoption of AI-driven autonomous diagnostics by operators
- Handling incomplete or ambiguous data in real-world environments
Validation Strategy
- Pilot deployments with telecom and datacenter operators to measure incident resolution time reduction
- Benchmarking against existing RCA tools for accuracy and maintenance overhead
- User feedback to refine investigation protocols and tool integrations
Research Paper Overview
Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure
Summary
Large-scale telecom and datacenter infrastructures face complex failure propagation across multi-layered service and resource models. Traditional root cause analysis methods are costly and inflexible. This work introduces an agentic diagnostic framework using a Large Language Model to autonomously investigate failures via structured tools, improving diagnosis accuracy and operational efficiency.