LG Uplus Accelerates AI Infrastructure Automation with AWS and Agentic Tech
LG Uplus is fundamentally changing how it manages its network and AI infrastructure. By moving away from traditional, fragmented development environments and embracing AI-driven automation, the Korean carrier is aiming for a future where networks are not just automated, but autonomous. From reducing customer complaints to streamlining the MLOps pipeline, the company is integrating generative AI and agent technologies to boost operational stability.
Bridging the Gap Between AI Development and Operation
On April 10, 2026, at the “2026 Modern Agentic Applications Day” hosted by Amazon Web Services (AWS), LG Uplus detailed its transition to a cloud-linked infrastructure. Historically, AI development often suffered from a disconnect between the creation of a model and its actual deployment in a live service. This gap led to repetitive tasks during learning, evaluation, and distribution.
To solve this, LG Uplus implemented an AWS EKS-based hybrid infrastructure architecture. By using AWS EKS (a managed Kubernetes service), the company connected its own on-premises GPU infrastructure to the EKS cluster. This integration allows the entire workflow—from data collection and training to deployment and operation—to function as a single, streamlined pipeline. This “Model Ready” state ensures that AI models can be applied to services immediately and consistently.
The Path to an Autonomous Network by 2028
While infrastructure stability is a priority, LG Uplus is also transforming its core network operations. The company’s goal is to achieve full network autonomy by 2028, shifting from simple task automation to proactive, AI-driven operations.
At the heart of this strategy is AION, an AI-driven network operations platform built on LG AI Research’s EXAONE large language model. AION is designed to handle several critical functions independently:
- Fault Management: Detecting and responding to network faults automatically.
- Traffic Optimization: Anticipating traffic surges during major events and adjusting base-station settings in advance.
- Quality Control: Detecting quality anomalies and managing overload control.
- Facility Oversight: Using LLM-powered inspection robots and digital twins to automate equipment checks and monitoring.
Measurable Impact on Service Quality
The shift toward AI agents is already yielding significant results in customer satisfaction. According to reports from RCR Wireless, the implementation of AI agents for automated fault detection and response has led to a substantial drop in service complaints:
- Mobile service complaints: Reduced by 70%
- Home service complaints: Reduced by 56%
Key Takeaways: LG Uplus AI Strategy
| Focus Area | Key Technology | Primary Goal |
|---|---|---|
| Infrastructure | AWS EKS Hybrid Architecture | Unified AI development and operation flow |
| Network Ops | AION (via EXAONE LLM) | Full network autonomy by 2028 |
| Maintenance | Digital Twins & AI Robots | Automated equipment monitoring |
Looking Ahead
By integrating generative AI into the very fabric of its infrastructure and network, LG Uplus is reducing the manual burden on its operators and developers. The move toward an autonomous network suggests a future where telecom infrastructure can self-heal and self-optimize in real-time, drastically reducing downtime and improving the finish-user experience.