Far EasTone Telecom (FET) is turning agentic AI into real operations impact
Far EasTone Telecom· TaiwanMicrosoft network operations agent (NOA) reference architecture
Far EasTone Telecom (FET), a Taiwanese operator, applied Microsoft's network operations agent (NOA) reference architecture to redefine cloud-native network operations by embedding agentic AI across its network operations center (NOC) and change-management workflows. Nearly 60% of FET's NOC operations are now AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents handle large-scale alarm correlation and root cause analysis in seconds, supporting nearly 7,000 monthly operational queries with an average response time of 16 seconds, and most maintenance actions complete within one minute, significantly reducing human error and accelerating recovery times.
Overview
Far EasTone Telecom (FET), a Taiwanese operator, applied Microsoft's network operations agent (NOA) reference architecture to redefine cloud-native network operations by embedding agentic AI across its network operations center (NOC) and change-management workflows. Nearly 60% of FET's NOC operations are now AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents handle large-scale alarm correlation and root cause analysis in seconds, supporting nearly 7,000 monthly operational queries with an average response time of 16 seconds, and most maintenance actions complete within one minute, significantly reducing human error and accelerating recovery times.
This entry has 10 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Telecom network operations face exploding event volumes, rising complexity, and persistent skills gaps, requiring operators to move from reactive management to autonomous, faster-responding operations.
The solution
Far EasTone Telecom (FET) applied Microsoft's network operations agent (NOA) reference architecture to redefine cloud-native network operations by embedding agentic AI across its network operations center (NOC) and change-management workflows. AI agents handle large-scale alarm correlation and root cause analysis in seconds.
Reported business value
Nearly 60% of FET's NOC operations are now AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents support nearly 7,000 monthly operational queries with an average response time of 16 seconds, and most maintenance actions complete within one minute, significantly reducing human error and accelerating recovery times, allowing engineers to focus on higher value work.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other telecommunications entries in the register.
AI serves QoS & operational efficiency
Orange France deployed AI-based tools across its network operations to improve quality of service and operational efficiency, including the Recovisu diagnostic tool that helps technicians correct errors during interventions, automated frequency declarations with the National Frequency Agency, AI-driven preventive maintenance for equipment such as antenna batteries, and an AI-evaluated citizen infrastructure-damage-reporting app. The NRJ5G app analyzed consumption data to prevent unnecessary mobile site electrical subscription upgrades, saving nearly 48 million.
Orange France· FranceRecovisu · NRJ5GDeutsche Telekom and Google Cloud Collaborate on MINDR Agentic AI for Autonomous Network Operations
Deutsche Telekom, in partnership with Google Cloud, announced MINDR (Multi-Agentic Intelligent Network Diagnostics & Remediation), a multi-agentic AI system built with Google Gemini models on Vertex AI and developed with Google Cloud's Autonomous Network Operations framework. MINDR correlates signals end-to-end across the RAN, transport, and core network domains to proactively identify service-impacting issues and support autonomous, explainable remediation, shifting operations from reactive troubleshooting to predictive automation. MINDR builds on Deutsche Telekom's RAN Guardian Agent, live in production in Germany since November 2025, which monitors network behavior during high-impact events and traffic surges. RAN Guardian autonomously triggered over 100 remediation actions at Christmas market events in its first month and reduced the time needed to manage major events from hours to around a minute (a more than 95% improvement). For 2026 it identified 237,000 events, including around 130 Carnival events each expected to draw over 10,000 participants, served by 611 mobile sites pre-checked for issues; only 5 sites experienced peak loads, which RAN Guardian optimized during the event. RAN Guardian is now scaling across Deutsche Telekom's European national companies, starting with the Czech Republic and Croatia. MINDR's first production releases are planned for later in 2026.
Chunghwa Telecom Builds Multiple Generative AI Solutions with Amazon Bedrock
Chunghwa Telecom, one of Taiwan's largest integrated telecom providers, migrated its generative AI projects to Amazon Bedrock and built three applications: a software development lifecycle documentation assistant, an interactive virtual English teacher using Amazon Titan and Stable Diffusion XL, and a generative AI marketing assistant built on Amazon Personalize.
BT Group writes and maintains 2 million lines of code a year using Amazon Q Developer
BT Group, a UK telecommunications company, rolled out Amazon Q Developer to around 2,000 active developers for AI-assisted coding, chat-based troubleshooting, automated unit test generation, and code transformation from older to newer, more secure versions.
Was this helpful?
Your feedback helps us improve our use case database